THE ROLE OF MEMORY IN LEARNING

The sensory register transfers information to  short-term memory (STM) , which corresponds roughly to awareness or what one is conscious of at a given moment. STM is limited in capacity. Miller ( 1956 ) proposed that it holds seven plus or minus two chunks (units) of information. A chunk is a meaningful item: a letter, word, number, or common expression (e.g., “bread and butter”). STM also is limited in duration; for chunks to be retained they must be rehearsed (repeated). Without rehearsal, information is lost after a few seconds. With development, children are able to hold more and larger chunks of information in memory (Cowan et al.,  2010 )

While information is in STM, related knowledge in  long-term memory (LTM) , or permanent memory, is activated and placed in STM to be integrated with the new information. To name all the state capitals beginning with the letter A, students recall the names of states—perhaps by region of the country—and scan the names of their capital cities. When students who do not know the capital of Maryland learn “Annapolis,” they can store it with “Maryland” in LTM.

It is debatable whether information is lost from LTM (i.e., forgotten). Some researchers contend that it can be, whereas others say that failure to recall reflects a lack of good retrieval cues rather than forgetting. If Sarah cannot recall her third-grade teacher’s name (Mapleton), she might be able to if given the hint, “Think of trees.” Regardless of theoretical perspective, researchers agree that information remains in LTM for a long time (see  Chapter 6 ).

Control (executive) processes  regulate the flow of information throughout the information processing system. Rehearsal is an important control process that occurs in STM. For verbal material, rehearsal takes the form of repeating information aloud or subvocally. Other control processes include coding (putting information into a meaningful context—an issue being discussed in the opening scenario), imaging (visually representing information), implementing decision rules, organizing information, monitoring level of understanding, and using retrieval, self-regulation, and motivational strategies.

The two-store model was a major advance in the field of information processing. Researchers showed that the two-store model could account for many research results. One of the most consistent research findings is that when people have a list of items to learn, they tend to recall best the initial items ( primacy effect ) and the last items ( recency effect ), as portrayed in  Figure 5.1 . As noted earlier, initial items receive the most rehearsal and are transferred to LTM, whereas the last items are still in STM at the time of recall. Middle items are recalled the poorest because they are no longer in  working memory  (WM) at the time of recall (having been pushed out by subsequent items), they receive fewer rehearsals than initial items, and they are not properly stored in LTM.

Other research suggested, however, that learning may be more complex than the basic two-store model stipulates (Baddeley,  1998 ). One problem is that this model does not fully specify how information moves from one stage of processing to another. The control processes notion is plausible but vague. We might ask: Why do some inputs proceed from the sensory registers into STM and others do not? Which mechanisms decide that information has been rehearsed long enough and transfer it into LTM? How is information in LTM selected to be activated? Another concern is that this model seems best suited to handle verbal material. How nonverbal representation occurs with material that may not be readily verbalized, such as modern art and well-established skills, is not clear.

The model also is vague about what really is learned. Consider people learning word lists. With nonsense syllables, they have to learn the words themselves and the positions in which they appear. When they already know the words, they must only learn the positions; for example, “cat” appears in the fourth position, followed by “tree.” People must take into account their purpose in learning and modify learning strategies accordingly. What mechanism controls these processes?

Whether all components of the system are used at all times is also an issue. STM is useful when people are acquiring knowledge and need to relate incoming information to knowledge in LTM. But we do many things automatically: get dressed, walk, ride a bicycle, respond to simple requests (e.g., “Do you have the time?”). For many adults, reading (decoding) and simple arithmetic computations are automatic processes that place little demand on cognitive processes. Such automatic processing may not require STM. How does automatic processing develop, and what mechanisms govern it?

These and other issues not addressed well by the two-store model (e.g., the role of motivation in learning and the development of self-regulation) have led to alternative models and modifications to the original model (Matlin,  2009 ; Nairne,  2002 ). Next we examine levels (or depth) of processing.

Levels (Depth) of Processing

Levels (depth) of processing  theory conceptualizes memory according to the type of processing that information receives rather than its location (Craik,  1979 ; Craik & Lockhart,  1972 ; Craik & Tulving,  1975 ; Lockhart, Craik, & Jacoby,  1976 ; Surprenant & Neath,  2009 ). This view does not incorporate stages or structural components such as STM or LTM (Surprenant & Neath,  2009 ). Rather, different ways to process information (such as levels or depth at which it is processed) exist: physical (surface), acoustic (phonological, sound), and  semantic  (meaning). These three levels are dimensional, with physical processing being the most superficial (such as “x” as a symbol devoid of meaning as discussed by the teachers in the introductory scenario) and semantic processing being the deepest. For example, suppose you are reading and the next word is wren. This word can be processed on a surface level (e.g., it is not capitalized), a phonological level (rhymes with den), or a semantic level (small bird). Each level represents a more elaborate (deeper) type of processing than the preceding level; processing the meaning of wrenexpands the information content of the item more than acoustic processing, which expands content more than surface-level processing.

These three levels seem conceptually similar to the sensory register, STM, and LTM of the two-store model. Both views contend that processing becomes more elaborate with succeeding stages or levels. Unlike the two-store model, levels of processing does not assume that the three types of processing constitute stages. In levels of processing, one does not have to move to the next process to engage in more elaborate processing; depth of processing can vary within a level. Wren can receive low-level semantic processing (small bird) or more extensive semantic processing (its similarity to and difference from other birds).

Another difference between the two information processing models concerns the order of processing. The two-store model assumes information is processed first by the sensory register, then by STM, and finally by LTM. The levels of processing model does not make a sequential assumption. To be processed at the meaning level, information does not have to be first processed at the surface and sound levels (beyond what processing is required for information to be received; Lockhart et al.,  1976 ).

The two models also have different views of how type of processing affects memory. In levels of processing, the deeper the level at which an item is processed, the better the memory because the memory trace is more ingrained. The teachers in the opening scenario are concerned about how they can help students process algebraic information at a deeper level. Once an item is processed at a particular point within a level, additional processing at that point should not improve memory. In contrast, the two-store model contends that memory can be improved with additional processing of the same type. This model predicts that the more a list of items is rehearsed, the better it will be recalled.

Some research evidence supports levels of processing. Craik and Tulving ( 1975 ) presented individuals with words. As each word was presented, they were given a question to answer. The questions were designed to facilitate processing at a particular level. For surface processing, people were asked, “Is the word in capital letters?” For phonological processing they were asked, “Does the word rhyme with train?” For semantic processing, “Would the word fit in the sentence, ‘He met a _____ in the street’?” The time people spent processing at the various levels was controlled. Their recall was best when items were processed at a semantic level, next best at a phonological level, and worst at a surface level. These results suggest that forgetting is more likely with shallow processing and is not due to loss of information from WM or LTM.

Levels of processing implies that student understanding is better when material is processed at deeper levels. Glover, Plake, Roberts, Zimmer, and Palmere ( 1981 ) found that asking students to paraphrase ideas while they read essays significantly enhanced recall compared with activities that did not draw on previous knowledge (e.g., identifying key words in the essays). Instructions to read slowly and carefully did not assist students during recall.

Despite these positive findings, levels of processing theory has problems. One concern is whether semantic processing always is deeper than the other levels. The sounds of some words (kaput) are at least as distinctive as their meanings (“ruined”). In fact, recall depends not only on level of processing but also on type of recall task. Morris, Bransford, and Franks ( 1977 ) found that, given a standard recall task, semantic coding produced better results than rhyming coding; however, given a recall task emphasizing rhyming, asking rhyming questions during coding produced better recall than semantic questions. Moscovitch and Craik ( 1976 ) proposed that deeper processing during learning results in a higher potential memory performance, but that potential will be realized only when conditions at retrieval match those during learning.

Another concern with levels of processing theory is whether additional processing at the same level produces better recall. Nelson ( 1977 ) gave participants one or two repetitions of each stimulus (word) processed at the same level. Two repetitions produced better recall, contrary to the levels of processing hypothesis. Other research shows that additional rehearsal of material facilitates retention and recall as well as automaticity of processing (Anderson,  1990 ; Jacoby, Bartz, & Evans,  1978 ).

A final issue concerns the nature of a level. Investigators have argued that the notion of depth is fuzzy, both in its definition and measurement (Surprenant & Neath,  2009 ; Terry,  2009 ). As a result, we do not know how processing at different levels affects learning and memory (Baddeley,  1978 ; Nelson,  1977 ). Time is a poor criterion of level because some surface processing (e.g., “Does the word have the following letter pattern: consonant-vowel-consonant-consonant-vowel-consonant?”) can take longer than semantic processing (“Is it a type of bird?”). Neither is processing time within a given level indicative of deeper processing (Baddeley,  1978 ,  1998 ). A lack of clear understanding of levels (depth) limits the usefulness of this perspective. We now turn to a contemporary perspective on information processing.

CONTEMPORARY INFORMATION PROCESSING MODEL

A contemporary, generic model of information processing is shown in  Figure 5.4 . This section gives an overview of the model; greater explanation is provided in the sections that follow.

Figure 5.4 Contemporary information processing model.

Key Processes

This model bears some similarity to the original Atkinson and Shiffrin ( 1968 ,  1971 ) model, but there are important differences. Based on years of research, the current model reflects key refinements in the operation of the information processing system.

Unlike the earlier model, the current one is not a stage model. There are phases of information processing such as perceiving and integrating new knowledge into LTM, but the system is dynamic, and rapid shifting among processes occurs. A second difference is that STM has been dropped as a separate memory in favor of working memory (WM). WM better reflects the dynamic nature of information processing and its interrelated functions with perception and LTM.

Third, the control processes have been dropped. Contemporary information processing theory addresses cognitive and motivational factors—such as goals, beliefs, and values—that focus learners’ attention and help them construct and process information in line with their goals, beliefs, and values (Mayer,  2012 ).

Finally, the contemporary model is less mechanistic and places great emphasis on the active construction of knowledge by learners (Mayer,  2012 ). Learners do not simply react to stimuli that impinge upon them but rather seek information that helps them learn. The current model reflects, in short, a large degree of learner control and self-regulation (see  Chapter 10 ).

The model assumes that information in memory begins as environmental sensory input. Sensory memory only holds information for milliseconds—long enough for the stimulus trace to be processed further. Of course, at any moment a lot of information is bombarding our sensory memories. Most of it is discarded, as much as 99% (Wolfe,  2010 ). This is desirable as most of it is irrelevant.

Inputs received by sensory memories, except for smells, are sent to the thalamus and then to the specific parts of the cortex designed to process those inputs (see  Chapter 2 ). At this early stage of processing, inputs are transformed from sensory information to perceptions that include meanings. A visual stimulus, for example, goes from being a visual light beam to “light from a flashlight.”

Next information is processed in WM. Perceptions are worked on (e.g., rehearsed, thought about) and integrated with information in LTM. Information that receives sufficient attention and rehearsal will be processed for transfer to LTM; information that is not adequately processed will be lost. Although WM functions may occur in different parts of the brain, the primary area seems to be the prefrontal cortex of the frontal lobe (Wolfe,  2010 ).

Information that is sufficiently constructed and processed is integrated with knowledge in LTM. Such consolidation occurs by forming or adapting existing neural networks or by strengthening existing ones. The process is dynamic because while WM is integrating with LTM it also is receiving new sensory inputs.

Knowledge Construction

Attention is important throughout the process, although attention is not always a conscious process. Attending to environmental inputs is necessary for them to enter the sensory registers. Some of this attention is conscious, as when learners direct their attention to computer screens. But much is not consciously driven (Dijksterhuis & Aarts,  2010 ); it is not possible to direct our attention to the multiple inputs that simultaneously impinge on us. Early attention is not selective; our reticular activating systems filter these stimuli, mostly without conscious awareness (Wolfe,  2010 ). Attention becomes more conscious with increased processing (Hübner, Steinhauser, & Lehle,  2010 ), through perception and especially as processing proceeds.

It was noted earlier that current information processing theory emphasizes learner control. The idea of knowledge construction is central. As Mayer ( 2012 ) explains: “Meaningful learning occurs when people engage in appropriate cognitive processing during learning, including selecting relevant information, organizing it into coherent mental representations, and integrating representations with each other and with relevant knowledge activated from long-term memory” (p. 89). Compared with earlier views that emphasized knowledge acquisition, contemporary theories stress knowledge construction by learners, or co-construction if others (e.g., teacher, peers) participate in the process (Mayer,  2012 ). The sections that follow provide elaborated descriptions of the processes discussed so far.

ATTENTION

The word  attention  is heard often in educational settings.  Attention  refers to concentrated mental activity that focuses on a limited amount of information in sensory memory and WM (Matlin,  2009 ). Teachers and parents complain that students do not pay attention to instruction or directions. (This does not seem to be the problem in the opening scenario; rather, the issue involves meaningfulness of processing.) Even high-achieving students do not always attend to instructionally relevant events. Sights, sounds, smells, tastes, and sensations bombard us; we cannot and should not attend to them all. Because our attentional capabilities are limited, attention can be construed as the process of selecting some of many potential inputs.

Alternatively, attention can refer to a limited human resource expended to accomplish one’s goals and to mobilize and maintain cognitive processes (Grabe,  1986 ). Attention is not a bottleneck in the information processing system through which only so much information can pass. Rather, it describes a general limitation on the entire human information processing system.

This section discusses conscious attention, which is necessary for learning. Conscious attention affects rehearsal in WM and the processes involved in integrating knowledge into LTM such as elaboration and organization. As mentioned earlier, most attention before inputs get to WM is unconscious (Wolfe,  2010 ). This section suggests ways that teachers can help focus students’ attention for learning which, although primarily involving conscious attention, also can help direct the more-unconscious aspects of students’ attention to inputs relevant to learning.

Theories of Attention

Researchers have explored how people select inputs for attending. In  dichotic listening  tasks, people wear headphones and receive different messages in each ear. They are asked to “shadow” one message (report what they hear); most can do this quite well. In an early study, Cherry ( 1953 ) investigated what happened to the unattended message. He found that listeners knew when it was present, whether it was a human voice or a noise, and when it changed from a male to a female voice. They typically did not know what the message was, what words were spoken, which language was being spoken, or whether words were repeated.

Broadbent ( 1958 ) proposed a model of attention known as  filter (bottleneck) theory . In this view, incoming information from the environment is held briefly in a sensory system. Based on their physical characteristics, pieces of information are selected for further processing by the perceptual system. Information not acted on by the perceptual system is filtered out—not processed beyond the sensory system. Attention is selective because of the bottleneck—only some messages receive further processing. In dichotic listening studies, filter theory proposes that listeners select a channel based on their instructions. They know some details about the other message because the physical examination of information occurs prior to filtering.

Subsequent work by Treisman ( 1960 ,  1964 ) identified problems with filter theory. Treisman found that during dichotic listening experiments, listeners routinely shifted their attention between ears depending on the location of the message they were shadowing. If they were shadowing the message coming into their left ear, and if that message suddenly shifted to the right ear, they continued to shadow the original message and not the new message coming into the left ear. Selective attention depends not only on the physical location of the stimulus but also on its meaning.

Treisman ( 1992 ; Treisman & Gelade,  1980 ) proposed a feature-integration theory. Sometimes we distribute attention across many sensory inputs, each of which receives low-level processing. At other times we focus on a particular sensory input, which is more cognitively demanding. Rather than blocking out messages, attention simply makes them less salient than those being attended to. Information inputs initially are subjected to different tests for physical characteristics and content. Following this preliminary analysis, one input may be selected for attention.

Treisman’s model is problematic in the sense that much analysis must precede attending to an input, which is puzzling because presumably the original analysis involves some conscious attention. Norman ( 1976 ) proposed that all inputs are attended to in sufficient fashion to activate a portion of LTM. At that point, one input is selected for further attention based on the degree of activation, which depends on the context. An input is more likely to be attended to if it fits into the context established by prior inputs. While people read, for example, many outside stimuli impinge on their sensory system, yet they attend to the printed symbols.

In Norman’s view, stimuli activate portions of LTM, but attention involves more complete activation. Neisser ( 1967 ) suggested that preattentive processes are involved in head and eye movements (e.g., refocusing attention) and in guided movements (e.g., walking, driving). Preattentive processes are automatic—people implement them without conscious mediation. In contrast, attentional processes are deliberate and require conscious activity. In support of this point, Logan ( 2002 ) postulated that attention and categorization occur together. As an object is attended to, it is categorized based on information in memory. Attention, categorization, and memory (WM and LTM) are three aspects of deliberate, conscious cognition.

Attention and Learning

Attention is necessary for learning. In learning to distinguish letters, a child learns the distinctive features: To distinguish b from d, students must attend to the position of the vertical line on the left or right side of the circle, not to the mere presence of a circle attached to a vertical line. To learn from a teacher, students must attend to the teacher’s voice and actions and ignore other inputs. To develop reading comprehension skills, students must attend to the printed words and ignore such irrelevancies as page size and color.

Learners consciously allocate attention to activities as a function of motivation and self-regulation (Kanfer & Ackerman,  1989 ; Kanfer & Kanfer,  1991 ). As skills become established, information processing requires less conscious attention. In learning to work multiplication problems, students must attend to each step in the process and check their computations. Once students learn multiplication tables and the algorithm, working problems becomes more automatic and is triggered by the input.

Differences in the ability to control attention are associated with student age, hyperactivity, intelligence, and learning disabilities (Grabe,  1986 ). Sustained attention is difficult for young children, as is attending to relevant rather than irrelevant information. Children also have difficulty switching attention rapidly from one activity to another. The ability to control attention contributes to the improvement of WM (Swanson,  2008 ). It behooves teachers to forewarn students of the attentional demands required to learn content. Outlines and study guides can serve as advance organizers and cue learners about the types of information that will be important. While students are working, teachers can use prompts, questions, and feedback to help students remain focused on the task (Meece,  2002 ).

Attention deficits are associated with learning problems. Hyperactive students are characterized by excessive motor activity, distractibility, and low academic achievement. They have difficulty focusing and sustaining attention on academic material. They may be unable to block out irrelevant stimuli, which overloads their WMs. Sustaining attention requires that students work in a strategic manner and monitor their level of understanding. Normal achievers and older children sustain attention better than do low achievers and younger learners on tasks requiring strategic processing (Short, Friebert, & Andrist,  1990 ).

Teachers can spot attentive students by noting their eye focus, their ability to begin working on cue (after directions are completed), and physical signs (e.g., writing, keyboarding) indicating they are engaged in work. But physical signs alone may not be sufficient; strict teachers can keep students sitting quietly even though students may not be engaged in class work.

Teachers can promote student attention to relevant material through the design of classroom activities ( Application 5.1 ). Eye-catching displays or actions at the start of lessons engage student attention. Teachers who move around the classroom help sustain student attention on the task. Other suggestions for focusing and maintaining student attention are given in  Table 5.1 .

Meaning and Importance

We are more likely to attend to inputs that have meaning than to those with less meaning (Wolfe,  2010 ). When sensory inputs enter WM, it attempts to find related information in LTM. When nothing relevant can be found, attention is likely to wane and be directed toward other inputs. The important role of meaningfulness in learning is exemplified in the opening vignette and discussed later in this chapter.

APPLICATION 5.1 Maintaining Student Attention

Various practices help keep classrooms from becoming predictable and repetitive, which decreases attention. Teachers can vary their presentations, materials used, student activities, and personal qualities such as dress and mannerisms. Lesson formats for young children should be kept short. Teachers can sustain a high level of activity through student involvement and by moving about to check on student progress.

As Ms. Keeling begins a language arts activity in her third-grade class, she asks students to point to the location of the activity in their books. She varies how she introduces activities: Sometimes she forms students into small groups, whereas at other times they work individually. She also varies how students’ answers are checked. Students might use hand signals or respond in unison, or individual students can answer and explain their answers. As students independently complete the exercise, she moves about the room, checks students’ progress, and assists those having difficulty learning or maintaining task focus.

A music teacher might increase student attention by using vocal exercises, singing certain selections, using instruments to complement the music, and adding movement to instruments. The teacher might combine activities or vary their sequence. Small tasks also can be varied to increase attention, such as the way a new music selection is introduced. The teacher might play the entire selection, then model by singing the selection, and then involve the students in the singing. Alternatively, for the last activity the teacher could divide the selection into parts, work on each of the small sections, and then combine these sections to complete the full selection.

Table 5.1 Ways to focus and maintain student attention.

Perceived importance also can help direct and sustain conscious attention. In reading, for example, students are more likely to recall important text elements than less important ones (R. Anderson,  1982 ; Grabe,  1986 ). Both good and poor readers locate important material and attend to it for longer periods (Ramsel & Grabe,  1983 ; Reynolds & Anderson,  1982 ). What distinguishes these readers is subsequent processing and comprehension. Perhaps poor readers, being more preoccupied with basic reading tasks (e.g., decoding), become distracted from important material and do not process it adequately for retention and retrieval. While attending to important material, good readers may be more apt to rehearse it, make it meaningful, and relate it to knowledge in LTM, all of which improve comprehension (Resnick,  1981 ).

The importance of text material can affect subsequent recall through differential attention (R. Anderson,  1982 ). Text elements apparently are processed at some minimal level so importance can be assessed. Based on this evaluation, the text element either is dismissed in favor of the next element (unimportant information) or receives additional attention (important information). Assuming attention is sufficient, the actual types of processing students engage in must differ to account for subsequent comprehension differences. Better readers may engage in automatic processing of text more often than poorer readers.

Hidi ( 1995 ) noted that attention is required during many phases of reading: processing orthographic features, extracting meanings, judging information for importance, and focusing on important information. This suggests that attentional demands vary considerably depending on the purpose of reading—for example, extracting details, comprehending, or new learning.

PERCEPTION

Perception  (or  pattern recognition ) refers to attaching meaning to environmental inputs received through the senses. For an input to be perceived, it must register in one or more of the sensory registers and be transferred to the appropriate brain structure. The input then is compared to knowledge in LTM. Sensory registers and the comparison process are discussed in this section.

Sensory Registers

Environmental inputs are received through the senses: vision, hearing, touch, smell, and taste. Each sense has its own register that holds information briefly in the same form in which it is received (Wolfe,  2010 ). Information stays in the sensory register for less than .25 second (Mayer,  2012 ). Some sensory input is transferred to WM for further processing. Other input is lost and replaced by new input. The sensory registers operate in parallel fashion because several senses can be engaged simultaneously and independently of one another. The two sensory memories that have been most extensively explored are  iconic  (vision) and echoic (hearing).

In a typical experiment to investigate iconic memory, a researcher presents learners with rows of letters briefly (e.g., 50 milliseconds) and asks them to report as many as they remember. They commonly report only four to five letters from an array. Early work by Sperling ( 1960 ) provided insight into iconic storage. Sperling presented learners with rows of letters, then cued them to report letters from a particular row. Sperling estimated that, after exposure to the array, they could recall about nine letters. Sensory memory could hold more information than was previously believed, but while participants were recalling letters, the traces of other letters quickly faded. Sperling also found that the more time between the end of a presentation of the array and the beginning of recall, the poorer was the recall. This finding supports the idea that the loss of a stimulus from a sensory register involves  trace decay . Sakitt ( 1976 ; Sakitt & Long,  1979 ) argued that the icon is located in the rods of the eye’s retina. It is debatable whether the icon is a memory store or a persisting image.

There is evidence for an echoic memory similar in function to iconic memory (Matlin,  2009 ). Early studies by Darwin, Turvey, and Crowder ( 1972 ) and by Moray, Bates, and Barnett ( 1965 ) yielded results comparable to Sperling’s ( 1960 ). Research participants heard three or four sets of recordings simultaneously and then were asked to report one. Findings showed that echoic memory is capable of holding more information than can be recalled. Similar to iconic information, traces of echoic information rapidly decay following removal of stimuli. The echoic decay is not quite as rapid as the iconic, but periods beyond 2 seconds between cessation of stimulus presentation and onset of recall produce poorer recall.

LTM Comparisons

Perception occurs through bottom-up and top-down processing (Matlin,  2009 ). In  bottom-up processing ,inputs received by sensory registers are transferred to WM for comparisons with information in LTM to assign meanings beyond the physical properties. Environmental inputs have tangible physical properties. Assuming normal color vision, everyone who looks at a yellow tennis ball will recognize it as a yellow object, but only those familiar with tennis will recognize it as a tennis ball. The types of information people have acquired account for the different meanings they assign to objects.

Perception is affected not only by objective characteristics but also by prior experiences and expectations.  Top-down processing  refers to the influence of our knowledge and beliefs on perception (Matlin,  2009 ). Motivational states also are important. Perception is affected by what we wish and hope to perceive (Balcetis & Dunning,  2006 ). We often perceive what we expect and fail to perceive what we do not expect. Have you ever thought you heard your name spoken, only to realize that another name was being called? While waiting to meet a friend at a public place or to pick up an order in a restaurant, you may hear your name because you expect to hear it. Also, people may not perceive things whose appearance has changed or that occur out of context. You may not recognize co-workers you meet at the beach because you do not expect to see them dressed in beach attire. Top-down processing often occurs with ambiguous stimuli or those registered only briefly (e.g., a stimulus spotted in the “corner of the eye”).

An information processing theory of perception is  template matching , which holds that people store  templates , or miniature copies of stimuli, in LTM. When they encounter a stimulus, they compare it with existing templates and identify it if a match is found. This view is appealing but problematic. People would need millions of templates stored in LTM to recognize everyone and everything in their environment. Such a large stock would exceed the brain’s capability. Template theory also does a poor job of accounting for stimulus variations. Chairs, for example, come in all sizes, shapes, colors, and designs; hundreds of templates would be needed just to perceive a chair.

The problems with templates can be solved by assuming that they can have some variation. Prototype theory addresses this.  Prototypes  are abstract forms that include the basic ingredients of stimuli (Matlin,  2009 ; Rosch,  1973 ). Prototypes are stored in LTM and are compared with encountered stimuli that are subsequently identified based on the prototype they match or resemble in form, smell, sound, and so on. Some research supports the existence of prototypes (Franks & Bransford,  1971 ; Posner & Keele,  1968 ; Rosch,  1973 ).

A major advantage of prototypes over templates is that each stimulus has only one prototype instead of countless variations; thus, identification of a stimulus should be easier because comparing it with several templates is not necessary. One issue with prototypes concerns the amount of acceptable stimulus variability, or how closely a stimulus must match a prototype to be identified as an instance of that prototype.

A variation of the prototype model involves feature analysis (Matlin,  2009 ). In this view, one learns the critical features of stimuli and stores these in LTM as images or verbal codes (Markman,  1999 ). When an input enters the sensory register, its features are compared with memorial representations. If enough of the features match, the stimulus is identified. For a chair, the critical features may be legs, seat, and a back. Many other features (e.g., color, size) are irrelevant. Any exceptions to the basic features need to be learned (e.g., bleacher and beanbag chairs that have no legs). Unlike the prototype analysis, information stored in memory is not an abstract representation of a chair but rather includes its critical features. One advantage of feature analysis is that each stimulus does not have just one prototype, which partially addresses the concern about the amount of acceptable variability. There is empirical research support for feature analysis (Matlin,  2009 ).

Treisman ( 1992 ) proposed that perceiving an object establishes a temporary representation in an object file that collects, integrates, and revises information about its current characteristics. The contents of the file may be stored as an object token. For newly perceived objects, we try to match the token to a memorial representation (dictionary) of object types, which may or may not succeed. The next time the object appears, we retrieve the object token, which specifies its features and structure. The token will facilitate perception if all of the features match but may impair it if many do not match.

Regardless of how LTM comparisons are made, research evidence supports the idea that perception depends on bottom-up and top-down processing (Anderson,  1980 ; Matlin,  2009 ; Resnick,  1985 ). In reading, for example, bottom-up processing analyzes features and builds a meaningful representation to identify stimuli. Beginning readers typically use bottom-up processing when they encounter letters and new words and attempt to sound them out. People also use bottom-up processing when experiencing unfamiliar stimuli (e.g., handwriting).

Reading would proceed slowly if all perception required analyzing features in detail. In top-down processing, individuals develop expectations regarding perception based on the context. Skilled readers build a mental representation of the context while reading and expect certain words and phrases in the text (Resnick,  1985 ). Effective top-down processing depends on extensive prior knowledge. We now turn to a discussion of encoding, a key process that occurs in WM.

ENCODING

Encoding  refers to the process of putting new incoming information into the information processing system and preparing it for storage in LTM. Once an input has been attended to, processed by sensory memory, and perceived, it enters WM. This section discusses WM and the influences on encoding.

Working Memory (WM)

Working memory  is our memory of immediate consciousness. Although WM functions may occur in different parts of the brain depending on the task to be performed, its primary activity seems to reside in the prefrontal cortex of the frontal lobe (Gazzaniga, Ivry, & Mangun,  1998 ; Wolfe,  2010 ). Some researchers (e.g, Baddeley,  2012 ) distinguish WM from STM, with the latter referring to the temporary storage of information and the former to storage and manipulation of knowledge. We will use the term “WM” since both WM and sensory memory are of short-term duration.

WM performs two critical functions: maintenance and retrieval (Baddeley,  1992 ,  1998 ,  2001 ; Terry,  2009 ; Unsworth & Engle,  2007 ). Incoming information is maintained in an active state for a short time and is worked on by being rehearsed or related to information retrieved from LTM. As students read, WM holds for a few seconds the last words or sentences they read. Students might try to remember a particular point by repeating it several times (rehearsal) or by asking how it relates to a topic discussed earlier (relate to information in LTM). As another example, assume that a student is multiplying 45 by 7. WM holds these numbers (45 and 7), along with the product of 5 and 7 (35), the number carried (3), and the answer (315). The information in WM (5 × 7 = ?) is compared with activated knowledge in LTM (5 × 7 = 35). Also activated in LTM is the multiplication algorithm, and these procedures direct the student’s actions.

WM often functions as a conduit for information to be transferred to or integrated with knowledge in LTM. But sometimes WM is the final destination for information, especially information that we use immediately. For example, if a friend verbalizes to you a phone number to call, you hold it in WM long enough to enter it on your phone. In this case, LTM is essentially “outsourced” to your phone.

The contemporary perspective on WM expands upon the limited conception of STM in early models, which was viewed primarily as a storage site. Conversely, WM both maintains and processes information (Barrouillet, Portrat, & Camos,  2011 ).

Research has provided a reasonably detailed picture of the operation of WM. WM is limited in duration: If not acted upon quickly, information in WM is lost. In a classic study (Peterson & Peterson,  1959 ), participants were presented with a nonsense syllable (e.g., khv), after which they performed an arithmetic task before attempting to recall the syllable. The purpose of the arithmetic task was to prevent learners from rehearsing the syllable, but because the numbers did not have to be stored, they did not interfere with storage of the syllable in WM. The longer participants spent on the distracting activity, the poorer was their recall of the nonsense syllable. These findings imply that WM is fragile; information is quickly lost if not learned well. In the preceding example if you were distracted before entering the number in your phone, you may not be able to recall it.

WM also is limited in capacity: It can hold only a small amount of information. As noted earlier, Miller ( 1956 ) suggested that the capacity of WM is seven plus or minus two items, where items are such meaningful units as words, letters, numbers, and common expressions. One can increase the amount of information by  chunking , or combining information in a meaningful fashion. The phone number 555-1960 consists of seven items, but it can easily be chunked to two as follows: “Triple 5 plus the year Kennedy was elected president.”

Sternberg’s ( 1969 ) research on memory scanning provides insight into how information is retrieved from WM. Participants were presented rapidly with a small number of digits that did not exceed the capacity of WM. They then were given a test digit and were asked whether it was in the original set. Because the learning was easy, participants rarely made errors; however, as the original set increased from two to six items, the time to respond increased about 40 milliseconds per additional item. Sternberg concluded that people retrieve information from active memory by successively scanning items.

Baddeley ( 1998 ,  2001 ,  2012 ) developed a WM model that includes a phonological loop, visuo-spatial sketch pad, and central executive ( Figure 5.5 ). The phonological loop processes auditory (speech-based) information and keeps it active through rehearsal. The visuo-spatial sketch pad is responsible for establishing and maintaining visual information (images). Presumably there are additional sensory functions performed in WM (i.e., taste, smell, touch), but the visual and auditory have received the most research attention. The central executive is essentially a controller of attention. This is critical for learning, since much learning requires sustained task attention.

The central executive directs the processing of information in WM, as well as the movement of knowledge into and out of WM (Baddeley,  1998 ,  2001 ,  2012 ). The middle part of  Figure 5.5  is a type of  episodic  buffer, where information from multiple modalities can be integrated. This area acts as a buffer when it links the information in WM, as well as WM with perception and LTM (Baddeley,  2012 ).

The central executive performs several functions (Baddeley,  2012 ). In addition to focusing attention, it divides attention as needed between two or more inputs (e.g., visual and auditory), and controls switching between tasks when necessary. The central executive also performs interfacing with LTM.

Figure 5.5 Model of working memory.

Source: Baddeley, Alan, Human Memory: Theory and Practice, 2nd Ed., © 1998. Reprinted and Electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.

The central executive is goal directed; it selects information relevant to people’s plans and intentions from the sensory registers. Information deemed important is rehearsed. Rehearsal can maintain information in WM and improve recall (Baddeley,  2001 ; Rundus,  1971 ; Rundus & Atkinson,  1970 ).

Environmental or self-generated cues activate a portion of LTM, which then is more accessible to WM. This activated memory holds a representation of events occurring recently, such as a description of the context and the content. It is debatable whether active memory constitutes a separate memory store or merely an activated portion of LTM. Under the activation view, rehearsal keeps information in WM. In the absence of rehearsal, information decays with the passage of time (Nairne,  2002 ). There is high interest on the operation of WM, and researchers continue to explore its processes (Baddeley,  2012 ; Davelaar, Goshen-Gottstein, Ashkenazi, Haarmann, & Usher,  2005 ).

WM plays a critical role in learning. Compared with normally achieving students, those with mathematical and reading disabilities show poorer WM operation (Andersson & Lyxell,  2007 ; Swanson, Howard, & Sáez,  2006 ). Like other information processing functions, WM improves with development. Executive processing of information becomes more efficient (Swanson,  2011 ). The capacity for maintenance (e.g., rehearsal) develops (Gaillard, Barrouillet, Jarrold, & Camos,  2011 ), as does the capability to keep goals in mind (Marcovitch, Boseovski, Knapp, & Kane,  2010 ).

A key instructional implication is not to overload students’ WMs by presenting too much material at once or too rapidly (see the section on Cognitive Load later in this chapter). Where appropriate, teachers can present information visually and verbally to ensure that students retain it in WM sufficiently long enough to further cognitively process (e.g., relate to information in LTM). Although the capacity of WM is limited (Cowan, Rouder, Blume, & Saults,  2012 ), there is evidence that its capacity can be improved through training (e.g., using span tasks, where learners are asked to recall increasingly longer lists), as well as its attentional control (Shipstead, Redick, & Engle,  2012 ).

Influences on Encoding

Encoding begins in WM and is accomplished by making new information meaningful and integrating it with known information in LTM. Although information need not be meaningful to be learned—one unfamiliar with geometry could memorize the Pythagorean theorem without understanding what it means—meaningfulness improves learning and retention.

Attending to and perceiving stimuli do not ensure that information processing will continue. Many things teachers say in class go unlearned (even though students attend to the teacher and the words are meaningful) because students do not continue to process the information in WM and encode it. Important factors that influence encoding are elaboration and organization ( Figure 5.4 ), which help to form schemas.

Elaboration.

Elaboration  is the process of expanding upon new information by adding to it or linking it to what one knows. Elaborations assist encoding and retrieval because they link the to-be-remembered information with other knowledge. Recently learned information is easier to access in this expanded memory network. Even when the new information is forgotten, people often can recall the elaborations (Anderson,  1990 ). As the introductory vignette illustrates, a problem that many students have in learning algebra is that they cannot elaborate the material because it is abstract and does not easily link with other knowledge.

Rehearsing information keeps it in WM but does not necessarily elaborate it. A distinction can be drawn between maintenance rehearsal (repeating information over and over) and elaborative rehearsal(relating the information to something already known). Students learning U.S. history can simply repeat “D-Day was June 6, 1944,” or they can elaborate it by relating it to something they know (e.g., “In 1944 Roosevelt was elected president for the fourth time”). With development, children become more proficient at elaborative rehearsal, which is desirable because it leads to better recall than maintenance rehearsal (Lehmann & Hasselhorn,  2010 ).

Mnemonic  strategies (see  Chapter 10 ) elaborate information in different ways. Once such strategy is to form the first letters into a meaningful sentence. For example, to remember the order of the planets from the sun you might learn the sentence, “My very educated mother just served us nectarines,” in which the first letters correspond to those of the planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune). You first recall the sentence and then reconstruct planetary order based on the first letters.

Students may be able to devise elaborations, but if they cannot, they do not need to labor needlessly when teachers can provide effective elaborations. To assist storage in memory and retrieval, elaborations must make sense. Elaborations that are too unusual may not be remembered. Precise, sensible elaborations facilitate memory and recall (Bransford et al.,  1982 ; Stein, Littlefield, Bransford, & Persampieri,  1984 ).

Organization.

Gestalt theory and research showed that well-organized material is easier to learn and recall (Katona,  1940 ). Miller ( 1956 ) argued that learning is enhanced by classifying and grouping bits of information into organized chunks. Memory research demonstrates that even when items to be learned are not organized, people often impose organization on the material, which facilitates recall (Matlin,  2009 ). Organized material improves memory because items are linked to one another systematically. Recall of one item prompts recall of items linked to it. Research supports the effectiveness of organization for encoding among children and adults (Basden, Basden, Devecchio, & Anders,  1991 ).

One way to organize material is to use a hierarchy into which pieces of information are integrated.  Figure 5.6  shows a sample hierarchy for animals. The animal kingdom as a whole is on top, and underneath are the major categories (e.g., mammals, birds, reptiles). Individual species are found on the next level, followed by breeds.

Other ways of organizing information include the use of mnemonic strategies ( Chapter 10 ) and mental imagery ( Chapter 6 ). Mnemonics enable learners to enrich or elaborate material, such as by forming the first letters of words to be learned into an acronym, familiar phrase, or sentence (Matlin,  2009 ). Some mnemonic techniques employ imagery; in remembering two words (e.g., honey and bread), one might imagine them interacting with each other (honey on bread). Using audiovisuals in instruction can improve students’ imagery.

Schemas.

Elaboration and organization help to form schemas. A  schema  (plural  schemas  or schemata) is a structure that organizes large amounts of information into a meaningful system. Schemas include our generalized knowledge about situations (Matlin,  2009 ). Schemas are plans we learn and use during our environmental interactions. Larger units are needed to organize propositions representing bits of information into a coherent whole (Anderson,  1990 ). Schemas assist us in generating and controlling routine sequential actions (Cooper & Shallice,  2006 ).

Figure 5.6 Memory network with hierarchical organization.

In a classic study, Bartlett ( 1932 ) found that schemas aid in comprehending information. A participant read a story about an unfamiliar culture, after which this person reproduced it for a second participant, who reproduced it for a third participant, and so on. By the time the story reached the 10th person, its unfamiliar context had been changed to one that participants were familiar with (e.g., a fishing trip). Bartlett found that as stories were repeated, they changed in predictable ways. Unfamiliar information was dropped, a few details were retained, and the stories became more like participants’ experiences. They altered incoming information to fit their preexisting schemas.

Any well-ordered sequence can be represented as a schema. One type of schema is “going to a restaurant.” The steps consist of activities such as being seated at a table, looking over a menu, ordering food, being served, having dishes picked up, receiving a bill, leaving a tip, and paying the bill. Schemas are important because they indicate what to expect in a situation. People recognize a problem when reality and schema do not match. Have you ever been in a restaurant where one of the expected steps did not occur (e.g., you received a menu but no one returned to take your order)?

Common educational schemas involve laboratory procedures, studying, and comprehending stories. When given material to read, students activate the type of schema they believe is required. If students are to read a passage and answer questions about main ideas, they may periodically stop and quiz themselves on what they believe are the main points (Resnick,  1985 ). Schemas have been investigated extensively in research on reading and writing (McVee, Dunsmore, & Gavelek,  2005 ).

Schemas assist encoding because they help elaborate new knowledge and integrate it into an organized, meaningful structure. When learning material, students attempt to fit information into the schema’s spaces. Less important or optional schema elements may or may not be learned. In reading works of literature, students who have formed the schema for a tragedy can fit the characters and actions of the story into the schema. They expect to find elements such as good versus evil, human frailties, and a dramatic denouement. When these events occur, they are fit into the schema students have activated for the story ( Application 5.2 ).

APPLICATION 5.2 Schemas

· Teachers can increase learning by helping students develop schemas. A schema is helpful when learning can occur by applying an ordered sequence of steps. An elementary teacher might teach the following schema to his or her children to assist their reading of unfamiliar words:

· ■ Read the word in the sentence to see what might make sense.

· ■ Look at the beginning and ending of the word—reading the beginning and the ending is easier than the whole word.

· ■ Think of words that would make sense in the sentence and that would have the same beginning and ending.

· ■ Sound out all the letters in the word.

· ■ If these steps do not help identify the word, look it up in a dictionary.

With some modifications, this schema can be used by students of any age.

Teachers might help their students learn to use a schema to locate answers to questions listed at the end of chapters, such as the following:

· ■ Read through all of the questions.

· ■ Read the chapter completely once.

· ■ Reread the questions.

· ■ Reread the chapter slowly, and use paper markers if you find a section that seems to fit with one of the questions.

· ■ Go back and match each question with an answer.

· ■ When you find the answer, write it and the question on your paper.

· ■ If you cannot find an answer, use your index to locate key words in the question.

· ■ If you still cannot locate the answer, ask the teacher for help.

Schemas may facilitate recall independently of their benefits on encoding. Anderson and Pichert ( 1978 ) presented college students with a story about two boys skipping school. Students were advised to read it from the perspective of either a burglar or a home buyer; the story had elements relevant to both. Students recalled the story and later recalled it a second time. For the second recall, half of the students were advised to use their original perspective and the other half the other perspective. On the second recall, students recalled more information relevant to the second perspective but not to the first perspective and less information unimportant to the second perspective that was important to the first perspective. Kardash, Royer, and Greene ( 1988 ) also found that schemas exerted their primary benefits at the time of recall rather than at encoding. Collectively, these results suggest that at retrieval, people recall a schema and attempt to fit elements into it. This reconstruction may not be accurate but will include most schema elements.  Production systems , which are discussed later, bear some similarity to schemas.

LONG-TERM MEMORY: STORAGE

Although our knowledge about LTM is limited because we do not have a window into the brain, neuroscience and psychological research has painted a reasonably consistent picture of the storage process. The characterization of LTM in this chapter involves a structure with knowledge being represented as locations or nodes in networks, with networks connected (associated) with one another. Note the similarity between these cognitive networks and the neural networks discussed in  Chapter 2 . When discussing networks, we deal primarily with declarative knowledge and procedural knowledge. Conditional knowledge is covered in  Chapter 7  along with metacognitive activities that monitor and direct cognitive processing. It is assumed that most knowledge is stored in LTM in verbal codes, but the role of imagery also is addressed in  Chapter 6 .

Propositions

The Nature of Propositions.

· Propositions are the basic units of knowledge and meaning in LTM (Anderson,  1990 ; Kosslyn,  1984 ). A  proposition  is the smallest unit of information that can be judged true or false. Each of the following is a proposition:

· ■ The Declaration of Independence was signed in 1776.

· ■ 2 + 2 = 4.

· ■ Aunt Frieda hates turnips.

· ■ I’m good in math.

· ■ The main characters are introduced early in a story.

These sample propositions can be judged true or false. Note, however, that people may disagree on their judgments. Carlos may believe that he is bad in math, but his teacher may believe that he is very good. Objective truth is not the criterion; rather, whether a true or false judgment is possible.

The exact nature of propositions is not well understood. Although they can be thought of as sentences, it is more likely that they are meanings of sentences (Anderson,  1990 ). Research supports the point that we store information in memory as propositions rather than as complete sentences. Kintsch ( 1974 ) gave participants sentences to read that were of the same length but varied in the number of propositions they contained. The more propositions contained in a sentence, the longer it took participants to comprehend it. This implies that, although students can generate the sentence, “The Declaration of Independence was signed in 1776,” what they most likely have stored in memory is a proposition containing only the essential information (Declaration of Independence—signed—1776). With certain exceptions (e.g., memorizing a poem), it seems that people usually store meanings rather than precise wordings.

Propositions form networks that are composed of individual nodes or locations. Nodes can be thought of as individual words, although their exact nature is unknown but probably abstract. For example, students taking a history class likely have a “history class” network comprising such nodes as “book,” “teacher,” “location,” “name of student who sits on their left,” and so forth.

Propositional Networks.

Propositions are formed according to a set of rules. Researchers disagree on which rules constitute the set, but they generally believe that rules combine nodes into propositions and, in turn, propositions into higher-order structures or  networks , which are sets of interrelated propositions.

Anderson’s ACT theory (Anderson,  1990 ,  1993 ,  1996 ,  2000 ; Anderson et al.,  2004 ; Anderson, Reder, & Lebiere,  1996 ) proposes an ACT-R (Adaptive Control of Thought-Rational) network model of LTM with a propositional structure. ACT-R is a model of cognitive architecture that attempts to explain how all components of the mind work together to produce coherent cognition (Anderson et al.,  2004 ). A proposition is formed by combining two nodes with a subject–predicate link, or association; one node constitutes the subject and another node the predicate. Examples are (implied information in parentheses): “Fred (is) rich” and “Shopping (takes) time.” A second type of association is the relation–argument link, where the relation is verb (in meaning) and the argument is the recipient of the relation or what is affected by the relation. Examples are “eat cake” and “solve puzzles.” Relation arguments can serve as subjects or predicates to form complex propositions. Examples are “Fred eat(s) cake,” and “solv (ing) puzzles (takes) time.”

Propositions are interrelated when they share a common element. Common elements allow people to solve problems, cope with environmental demands, draw analogies, and so on. Without common elements, transfer would not occur; all knowledge would be stored separately, and information processing would be slow. One would not recognize that knowledge relevant to one domain is also relevant to other domains.

Figure 5.7  shows an example of a propositional network. The common element is “cat” because it is part of the propositions, “The cat walked across the front lawn,” and “The cat caught a mouse.” One can imagine that the former proposition is linked with other propositions relating to houses, whereas the latter is linked with propositions about mice.

Figure 5.7 Sample propositional network.

Evidence suggests that propositions are organized in hierarchical structures. Collins and Quillian ( 1969 ) showed that people store information at the highest level of generality. For example, the LTM network for “animal” would have stored at the highest level such facts as “moves” and “eats.” Under this category would come such species as “birds” and “fish.” Stored under “birds” are “has wings,” “can fly,” and “has feathers” (although there are exceptions—chickens are birds but they do not fly). The fact that birds eat and move is not stored at the level of “bird” because that information is stored at the higher level of animal. Collins and Quillian found that retrieval times increased the farther apart concepts were stored in memory.

The hierarchical organization idea has been modified by research showing that information is not always hierarchical. Thus, “collie” is closer to “mammal” than to “animal” in an animal hierarchy, but people are quicker to agree that a collie is an animal than to agree that it is a mammal (Rips, Shoben, & Smith,  1973 ).

Furthermore, familiar information may be stored both with its concept and at the highest level of generality (Anderson,  1990 ). If you have a bird feeder and you often watch birds eating, you might have “eat” stored with both “birds” and “animals.” This finding does not detract from the central idea that propositions are organized and interconnected. Although some knowledge may be hierarchically organized, much information is probably organized in a less systematic fashion in propositional networks.

Storage of Knowledge

Declarative Knowledge.

The major types of knowledge are declarative and procedural ( Figure 5.4 ).  Declarative knowledge , or knowing that something is the case, includes facts, beliefs, opinions, generalizations, theories, hypotheses, and attitudes about oneself, others, and world events (Gupta & Cohen,  2002 ; Paris, Lipson, & Wixson,  1983 ). It is acquired when a new proposition is stored in LTM, usually in a related propositional network (Anderson,  1990 ). ACT theory postulates that declarative knowledge is represented in chunks comprising the basic information plus related categories (Anderson,  1996 ; Anderson, Reder, & Lebiere,  1996 ).

The storage process operates as follows. First, the learner receives new information, such as when the teacher makes a statement or the learner reads a sentence. Next, the new information is parsed into one or more propositions in the learner’s WM. At the same time, related propositions in LTM are activated. The new propositions are associated with the related propositions in WM through the process of spreading activation (discussed in the following section). As this point, learners might generate additional propositions. Finally, all the new propositions—those received and those generated by the learner—are stored together in LTM (Hayes-Roth & Thorndyke,  1979 ).

Figure 5.8  illustrates this process. Assume that a teacher is presenting a unit on the U.S. Constitution and says to the class, “The vice president of the United States serves as president of the Senate but does not vote unless there is a tie.” This statement may activate other propositional knowledge stored in students’ memories relating to the vice president (e.g., elected with the president, becomes president when the president dies or resigns, can be impeached for crimes of treason) and the Senate (e.g., 100 members, two elected from each state, 6-year terms). Putting these propositions together, the students should infer that the vice president would vote if 50 senators voted for a bill and 50 voted against it.

Figure 5.8 Storage of declarative knowledge.

Note: Dotted lines represent new knowledge; solid lines indicate knowledge in long-term memory.

Storage problems can occur when students have no preexisting propositions with which to link new information. Students who have not heard of the U.S. Constitution or do not know what a constitution is will draw a blank when they hear the word for the first time. Conceptually meaningless information can be stored in LTM, but students learn better when new information is related to something they know. Showing students a facsimile of the U.S. Constitution or relating it to something they have studied (e.g., Declaration of Independence) gives them a referent to link with the new information.

Even when students have studied related material, they may not automatically link it with new information. Often the links need to be made explicit. When discussing the function of the vice president in the Senate, teachers could remind students of the composition of the U.S. Senate and the other roles of the vice president. Propositions sharing a common element are linked in LTM only if they are active in WM simultaneously. This point helps to explain why students might fail to see how new material relates to old material, even though the link is clear to the teacher. Instruction that best establishes propositional networks in learners’ minds incorporates review, organization of material, and reminders of things they know but are not thinking of now.

As with many memory processes, meaningfulness, organization, and elaboration facilitate storing information in memory. Meaningfulness is important because meaningful information can be easily associated with preexisting information in memory. Consequently, less rehearsal is necessary, which saves space and time of information in WM. The students being discussed in the opening scenario are having a problem making algebra meaningful, and the teachers express their frustration at not teaching the content in a meaningful fashion.

A study by Bransford and Johnson ( 1972 ) provides a dramatic illustration of the role of meaningfulness in storage and comprehension. Consider the following passage:

·  First you arrange things into different groups … One pile may be sufficient depending on how much there is to do. If you have to go somewhere else due to lack of facilities that is the next step…, It is better to do too few things at once than too many … At first the whole procedure will seem complicated. Soon, however, it will become just another facet of life … After the procedure is completed one arranges the materials into different groups again. Then they can be put into their appropriate places. Eventually they will be used once more and the whole cycle will then have to be repeated. (p. 722)

Without prior knowledge this passage is difficult to comprehend and store in memory because relating it to existing knowledge in memory is hard to do. However, knowing that it is about “washing clothes” makes remembering and comprehension easier. Bransford and Johnson found that students who knew the topic recalled about twice as much as those who were unaware of it. The importance of meaningfulness in learning has been demonstrated in numerous other studies (Anderson,  1990 ; Chiesi, Spilich, & Voss,  1979 ; Spilich, Vesonder, Chiesi, & Voss,  1979 ).

Organization facilitates storage because well-organized material is easier to relate to preexisting memory networks than is poorly organized material (Anderson,  1990 ). To the extent that material can be organized into a hierarchical arrangement, it provides a ready structure to be accepted into LTM. Without an existing LTM network, creating a new LTM network is easier with well-organized information than with poorly organized information.

Elaboration improves storage because it helps learners relate information to something they know. Through spreading activation, the elaborated material may be quickly linked with information in memory. For example, a teacher might be discussing the Mt. Etna volcano. Students who can elaborate that knowledge by relating it to their personal knowledge of volcanoes (e.g., Mt. St. Helens) will be able to associate the new and old information in memory and better retain the new material.

Spreading Activation.

Spreading activation  helps to explain how new information is linked to knowledge in LTM (Anderson,  1983 ,  1984 ,  1990 ,  2000 ; Collins & Loftus,  1975 ). The basic underlying principles are as follows (Anderson,  1984 ):

· ■ Human knowledge can be represented as a network of nodes, where nodes correspond to concepts and links to associations among these concepts.

· ■ The nodes in this network can be in various states that correspond to their levels of activation. More active nodes are processed “better.”

· ■ Activation can spread along these network paths by a mechanism whereby nodes can cause their neighboring nodes to become active. (p. 61)

Anderson ( 1990 ) cited the example of an individual presented with the word dog. This word is associatively linked with such other concepts in the individual’s LTM as bone, cat, and meat. In turn, each of these concepts is linked to other concepts. The activation of dog in LTM will spread beyond dog to linked concepts, with the spread lessening with concepts farther away from dog.

Spreading activation is based on the idea that memory structures vary in their  activation level  (Anderson,  1990 ). In this view, we do not have separate memory structures or phases but rather one memory with different activation states. Information may be in an active or inactive state. When active, the information can be accessed quickly. The active state is maintained as long as information is attended to. Without attention, the activation level will decay, in which case the information can be activated when the memory is reactivated (Collins & Loftus,  1975 ).

Active information can include information entering the information processing system and information that has been stored in memory (Baddeley,  1998 ). Regardless of the source, active information either is currently being processed or can be processed rapidly. Active material is roughly synonymous with WM, but the former category is broader than the latter. WM includes information in immediate consciousness, whereas active memory includes that information plus material that can be accessed easily. For example, if I am visiting Aunt Frieda and we are admiring her flower garden, that information is in WM, but other information associated with Aunt Frieda’s yard (trees, shrubs, dog) may be in an active state.

Rehearsal allows information to be maintained in an active state (Anderson,  1990 ). As with WM, only a limited amount of memory can be active at a given time. As one’s attention shifts, activation level changes.

Experimental support for the existence of spreading activation was obtained by Meyer and Schvaneveldt ( 1971 ). These investigators used a reaction time task that presented participants with two strings of letters and asked them to decide whether both were words. Words associatively linked (bread, butter) were recognized faster than words not linked (nurse, butter).

Spreading activation results in a larger portion of LTM being activated than knowledge immediately associated with the content of WM. Activated information stays in LTM unless it is deliberately accessed, but this information is more readily accessible to WM. Spreading activation also facilitates transfer of knowledge to different domains. Transfer depends on propositional networks in LTM being activated by the same cue, so students recognize that knowledge is applicable in the domains.

One advantage of activation level is that it can explain retrieval of information from memory. By dispensing with the notion of memory phases, the model eliminates the potential problem of transferring information. WM is that part of memory that is currently active. Activation decays with the passage of time, unless rehearsal keeps the information activated (Nairne,  2002 ).

At the same time, activation level has not escaped the dual-store model’s problems because it too dichotomizes the information system (active-inactive). We also have the problem of the strength level needed for information to pass from one state to another. Thus, we intuitively know that information may be partially activated (e.g., a word on the “tip of your tongue”—you know it but cannot recall it), so we might ask how much activation is needed for material to be considered active. These concerns notwithstanding, activation level and spreading activation offer important insights into the processing of information.

Schemas.

Propositional networks represent small pieces of knowledge.  Schemas  (or schemata) are large networks that represent the structure of objects, persons, and events (Anderson,  1990 ). Structure is represented with a series of “slots,” each of which corresponds to an attribute. In the schema or slot for houses, some attributes (and their values) might be as follows: material (wood, brick), contents (rooms), and function (human dwelling). Schemas are hierarchical; they are joined to superordinate ideas (building) and subordinate ones (roof).

Brewer and Treyens ( 1981 ) found research support for the underlying nature of schemas. Individuals were asked to wait in an office for a brief period, after which they were brought into a room where they wrote down everything they could recall about the office. Recall reflected the strong influence of a schema for office. They correctly recalled the office having a desk and a chair (typical attributes) but not that the office contained a skull (nontypical attribute). Books are a typical attribute of offices; although the office had no books, many persons incorrectly recalled books.

Schemas are important during teaching and for transfer (Matlin,  2009 ). Once students learn a schema, teachers can activate this knowledge when they teach any content to which the schema is applicable. Suppose an instructor teaches a general schema for describing geographical formations (e.g., mountain, volcano, glacier, river). The schema might contain the following attributes: height, material, and activity. Once students learn the schema, they can employ it to categorize new formations they study. In so doing, they would create new schemata for the various formations.

Procedural Knowledge.

Procedural knowledge , or knowledge of how to perform cognitive activities (Anderson,  1990 ; Gupta & Cohen,  2002 ; Hunt,  1989 ; Paris et al.,  1983 ), is central to much school learning. We use procedural knowledge to solve mathematical problems (e.g., algorithms), summarize information, skim passages, surf the Web, and perform laboratory techniques.

Procedural knowledge may be stored in networks as verbal codes and images, much the same way as declarative knowledge is stored. ACT theory posits that procedural knowledge is stored as a production system (Anderson,  1996 ; Anderson, Reder, & Lebiere,  1996 ). A  production system  (or  production ) is a network of condition–action sequences (rules), in which the condition is the set of circumstances that activates the system and the action is the set of activities that occurs (Anderson,  1990 ; Andre,  1986 ; see next section). Production systems seem conceptually similar to neural networks (discussed in  Chapter 2 ).

Production Systems and Connectionist Models

Production systems and connectionist models provide paradigms for examining the operation of cognitive learning processes (Anderson,  1996 ,  2000 ; Smith,  1996 ). To date, there has been little research on connectionist models that is relevant to education. Additional sources provide further information about these models (Bourne,  1992 ; Farnham-Diggory,  1992 ; Matlin,  2009 ; Siegler,  1989 ).

Production Systems.

ACT—an activation theory—specifies that a  production system  (or  production ) is a network of condition–action sequences (rules), in which the condition is a set of circumstances that activates the system and the action is the set of activities that occurs (Anderson,  1990 ,  1996 ,  2000 ; Anderson, Reder, & Lebiere,  1996 ; Andre,  1986 ). A production consists of if–then statements: If statements (the condition) include the goal and test statements, and then statements are the actions. As an example:

· ■ IF I see two numbers and they must be added,

· ■ THEN decide which is larger and start with that number and count up to the next one. (Farnham-Diggory,  1992 , p. 113)

Although productions are forms of procedural knowledge that can have conditions attached to them, they also include declarative knowledge.

Learning procedures for performing skills often occurs slowly (J. Anderson,  1982 ). First, learners represent a sequence of actions in terms of declarative knowledge. Each step in the sequence is represented as a proposition. Learners gradually drop out individual cues and integrate the separate steps into a continuous sequence of actions. For example, children learning to add a column of numbers will perform each step slowly, possibly even verbalizing it aloud. As they become more skillful, adding becomes part of an automatic, smooth sequence that occurs rapidly and without deliberate, conscious attention. Automaticity is a central feature of many cognitive processes (e.g., attention, retrieval; Moors & De Houwer,  2006 ). When processes become automatic, this allows the processing system to devote itself to complex parts of tasks ( Chapter 7 ).

A major constraint on skill learning is the size limitation of WM (Baddeley,  2001 ). Procedures would be learned quicker if WM could simultaneously hold all the declarative knowledge propositions. Because it cannot, students must combine propositions slowly and periodically stop and think (e.g., “What do I do next?”). WM contains insufficient space to create large procedures in the early stages of learning. As propositions are combined into small procedures, the latter are stored in WM simultaneously with other propositions. In this fashion, larger productions are gradually constructed.

These ideas explain why skill learning proceeds faster when students can perform the prerequisite skills (i.e., when they become automatic). When the latter exist as well-established productions, they are activated in WM at the same time as new propositions to be integrated. In learning to solve long-division problems, students who know how to multiply simply recall the procedure when necessary; it does not have to be learned along with the other steps in long division. Although this does not seem to be the problem in the opening scenario, learning algebra is difficult for students with basic skill deficiencies (e.g., addition, multiplication), because even simple algebra problems become difficult to answer correctly. Many children with reading disabilities seem to lack the capability to effectively process and store information at the same time (de Jong,  1998 ).

In some cases, specifying the steps in detail is difficult. For example, thinking creatively may not follow the same sequence for each student. Teachers can model creative thinking to include such self-questions as, “Are there any other possibilities?” Whenever steps can be specified, teacher demonstrations of the steps in a procedure, followed by student practice, are effective (Rosenthal & Zimmerman,  1978 ).

One problem with the learning of procedures is that students might view them as lockstep sequences to be followed regardless of whether they are appropriate. Gestalt psychologists showed how  functional fixedness , or an inflexible approach to a problem, hinders problem solving (Duncker,  1945 ;  Chapter 7 ). Adamantly following a sequence while learning may assist its acquisition, but learners also need to understand the circumstances under which other methods are more efficient.

Sometimes students overlearn skill procedures to the point that they avoid using alternative, easier procedures. At the same time, there are few, if any, alternatives for many of the procedures students learn (e.g., decoding words, adding numbers, determining subject–verb agreement). Overlearning these skills to the point of automatic production becomes an asset to students and makes it easier to learn new skills (e.g., drawing inferences, writing term papers) that require mastery of these basic skills.

One might argue that teaching problem-solving or inference skills to students who are deficient in basic mathematical facts and decoding skills, respectively, makes little sense. Research shows that poor grasp of basic number facts is related to low performance on complex arithmetic tasks (Romberg & Carpenter,  1986 ), and slow decoding relates to poor comprehension (Calfee & Drum,  1986 ; Perfetti & Lesgold,  1979 ). Not only is skill learning affected, but self-efficacy ( Chapter 4 ) suffers as well.

Practice is essential to instate basic procedural knowledge (Lesgold,  1984 ). In the early stages of learning, students require corrective feedback highlighting the portions of the procedure they implemented correctly and those requiring modification. Often students learn some parts of a procedure but not others. As students gain skill, teachers can point out their progress in solving problems quicker or more accurately.

Transfer of procedural knowledge occurs when the knowledge is linked in LTM with different content. Transfer is aided by having students apply the procedures to the different content and altering the procedures as necessary. General problem-solving strategies ( Chapter 7 ) are applicable to varied academic content. Students learn about their generality by applying them to different subjects (e.g., reading, mathematics).

Productions are relevant to cognitive learning, but several issues need to be addressed. ACT theory posits a single set of cognitive processes to account for diverse phenomena (Matlin,  2009 ). This view conflicts with other cognitive perspectives that delineate different processes depending on the type of learning (Shuell,  1986 ). Rumelhart and Norman ( 1978 ) identified three types of learning.  Accretion  involves encoding new information in terms of existing schemata;  restructuring  (schema creation) is the process of forming new schemata; and  tuning  (schema evolution) refers to the slow modification and refinement of schemata that occurs when using them in various contexts. These involve different amounts of practice: much for tuning and less for accretion and restructuring.

ACT is essentially a computer program designed to simulate learning in a coherent manner. As such, it may not address the range of factors involved in human learning. One issue concerns how people know which production to use in a given situation, especially if situations lend themselves to different productions being employed. Productions may be ordered in terms of likelihood, but a means for deciding what production is best given the circumstance must be available. Also of concern is the issue of how productions are altered. For example, if a production does not work effectively, do learners discard it, modify it, or retain it but seek more evidence? What is the mechanism for deciding when and how productions are changed?

Another concern relates to Anderson’s ( 1983 ,  1990 ) claim that productions begin as declarative knowledge. This assumption seems too strong given evidence that this sequence is not always followed (Hunt,  1989 ). Because representing skill procedures as pieces of declarative knowledge is essentially a way station along the road to mastery, one might question whether students should learn the individual steps. The individual steps eventually will not be used, so time may be better spent allowing students to practice them.

Finally, one might question whether production systems, as generally described, are nothing more than elaborate stimulus-response (S-R) associations (Mayer,  1992 ). Propositions (bits of procedural knowledge) become linked in memory and formed into networks so that when one piece is cued, others also are activated. Anderson ( 1983 ) acknowledged the associationist nature of productions but believes they are more advanced than simple S-R associations because they incorporate goals. In support of this point, ACT associations are analogous to neural network connections ( Chapter 2 ). Perhaps, as is the case with behavior theories, ACT can explain performance better than it can explain learning. These and other questions (e.g., the role of motivation) need to be addressed to establish the usefulness of productions in education better.

Connectionist Models.

Connectionist models  (or  connectionism , not to be confused with Thorndike’s connectionism discussed in  Chapter 3 ; Baddeley,  1998 ; Farnham-Diggory,  1992 ; Matlin,  2009 ; Smith,  1996 ) represent a more recent line of theorizing about complex cognitive processes. Like productions, connectionist models represent computer simulations of learning processes. These models link learning to neural system processing where impulses fire across synapses to form connections ( Chapter 2 ). The assumption is that higher-order cognitive processes are formed by connecting a large number of basic elements such as neurons (Anderson,  1990 ,  2000 ; Anderson, Reder, & Lebiere,  1996 ; Bourne,  1992 ). Connectionist models include distributed representations of knowledge (i.e., spread out over a wide network), parallel processing (many operations occur at once), and interactions among large numbers of simple processing units (Siegler,  1989 ). Connections may be at different stages of activation (Smith,  1996 ) and linked to input into the system, output, or one or more in-between layers.

Rumelhart and McClelland ( 1986 ) described a system of parallel distributed processing (PDP). This model is useful for making categorical judgments about information in memory. These authors provided an example involving two gangs and information about gang members, including age, education, marital status, and occupation. In memory, the similar characteristics of each individual are linked. For example, Members 2 and 5 would be linked if they both were about the same age, married, and engaged in similar gang activities. To retrieve information about Member 2, we could activate the memory unit with the person’s name, which in turn would activate other memory units. The pattern created through this spread of activation corresponds to the memory representation for the individual. Borowsky and Besner ( 2006 ) described a PDP model for making lexical decisions (e.g., deciding whether a stimulus is a word).

Connectionist units bear some similarity to productions in that both involve memory activation and linked ideas. At the same time, differences exist. In connectionist models all units are alike, whereas productions contain conditions and actions. Units are differentiated in terms of pattern and degree of activation. Another difference is that whereas productions are governed by rules, connectionism has no set rules. Neurons “know” how to activate patterns; after the fact we may provide a rule as a label for the sequence (e.g., rules for naming patterns activated; Farnham-Diggory,  1992 ).

One problem with the connectionist approach is explaining how the system knows which of the many units in memory to activate and how these multiple activations become linked in integrated sequences. This process seems straightforward in the case of well-established patterns; for example, neurons “know” how to react to a ringing phone, a cold wind, and a teacher announcing, “Everyone pay attention!” With less-established patterns the activations may be problematic. We also might ask how neurons become self-activating in the first place. This question is important because it helps to explain the role of connections in learning and memory. Although the notion of connections seems plausible and grounded in what we know about neurological functioning ( Chapter 2 ), to date this model has been more useful in explaining perception rather than learning and problem solving (Mayer,  1992 ). The latter applications are critical for education.

INSTRUCTIONAL APPLICATIONS

Information processing principles increasingly have been applied to educational settings. Three instructional applications that reflect information processing principles are advance organizers, the conditions of learning, and cognitive load.

Advance Organizers

Advance organizers  are broad statements presented at the outset of lessons that help to connect new material with prior learning (Mayer,  1984 ). Organizers direct learners’ attention to important concepts to be learned, highlight relationships among ideas, and link new material to what students know (Faw & Waller,  1976 ). Organizers also can be maps that are shown with accompanying text (Verdi & Kulhavy,  2002 ). It is assumed that learners’ LTMs are organized such that inclusive concepts subsume subordinate ones. Organizers provide information at high (inclusive) levels.

The conceptual basis of organizers derives from Ausubel’s ( 1963 ,  1968 ,  1977 ,  1978 ; Ausubel & Robinson,  1969 ) theory of  meaningful reception learning . Learning is meaningful when new material bears a systematic relation to relevant concepts in LTM; that is, new material expands, modifies, or elaborates information in memory. Meaningfulness also depends on personal variables such as age, background experiences, socioeconomic status, and educational background.

Ausubel advocated deductive teaching: General ideas are taught first, followed by specific points. This requires teachers to help students break ideas into smaller, related points and to link new ideas to similar content in memory. In information processing terms, the aims of the model are to expand propositional networks in LTM by adding knowledge and to establish links between networks.

Advance organizers can be expository or comparative.  Expository organizers  provide students with new knowledge needed to comprehend the lesson. Expository organizers include concept definitions and generalizations. Concept definitions state the concept, a superordinate concept, and characteristics of the concept. In presenting the concept “warm-blooded animal,” a teacher might define it (i.e., animal whose internal body temperature remains relatively constant), relate it to superordinate concepts (animal kingdom), and give its characteristics (birds, mammals).  Generalizations  are broad statements of general principles from which hypotheses or specific ideas are drawn. A generalization appropriate for the study of terrain would be: “Less vegetation grows at higher elevations.” Teachers can present examples of generalizations and ask students to think of others.

Comparative organizers  introduce new material by drawing analogies with familiar material. Comparative organizers activate and link networks in LTM. If a teacher were giving a unit on the body’s circulatory system to students who have studied communication systems, the teacher might relate the circulatory and communication systems with relevant concepts such as the source, medium, and target. For comparative organizers to be effective, students must have a good understanding of the material used as the basis for the analogy. Learners also must perceive the analogy easily. Difficulty perceiving analogous relationships impedes learning.

Organizers can promote learning and, because they help students relate content to a broader set of experiences, may facilitate transfer (Ausubel,  1978 ; Faw & Waller,  1976 ; Mautone & Mayer,  2007 ). Maps are especially effective organizers and lend themselves well to infusion in lessons via technology (Verdi & Kulhavy,  2002 ). Some examples of organizers are given in  Application 5.3 .

Conditions of Learning

Gagné ( 1985 ) formulated an instructional theory that reflects information processing principles. This theory highlights the  conditions of learning , or the circumstances that prevail when learning occurs (Ertmer, Driscoll, & Wager,  2003 ). Two steps are critical. The first is to specify the type of learning outcome; Gagné identified five major types (discussed later). The second is to determine the events of learning, or factors that make a difference in instruction.

APPLICATION 5.3 Advance Organizers

Advance organizers help students connect new material with prior learning. Ms. Lowery, a fourth-grade teacher, is working with her students to develop comprehensive paragraphs. The students have been learning to write descriptive and interesting sentences. Ms. Lowery projects the students’ sentences onto a screen and uses them as an organizer to show how to put sentences together to create a complete paragraph.

Mr. Oronsco, a middle school teacher, employed an organizer during geography. He began a lesson on landforms (surfaces with characteristic shapes and compositions) by reviewing the definition and components of geography concepts previously discussed. He wanted to show that geography includes elements of the physical environment, human beings and the physical environment, and different world regions and their ability to support human beings. To do this, Mr. Oronsco initially focused on elements of the physical environment and then moved to landforms. He discussed types of landforms (e.g., plateaus, mountains, hills) by showing mock-ups and asking students to identify key features of each landform. This approach gave students an overall framework or outline into which they could integrate new knowledge about the components.

A science instructor teaching the effects of blood disorders might begin by reviewing the basic parts of blood (e.g., plasma, white and red cells, platelets). Then the instructor could list various categories of blood disease (e.g., anemia, bleeding and bruising, leukemia, bone marrow disease). The students can build on this outline by exploring the diseases in the different categories and by studying the symptoms and treatments for each condition.

Table 5.2 Learning outcomes in Gagné’s theory.

Learning Outcomes.

Gagné ( 1984 ) identified five types of learning outcomes: intellectual skills, verbal information, cognitive strategies, motor skills, and attitudes ( Table 5.2 ).

Intellectual skills include rules, procedures, and concepts. They are forms of procedural knowledge or productions. This type of knowledge is employed in speaking, writing, reading, solving mathematical problems, and applying scientific principles to problems.

Verbal information, or declarative knowledge, is knowledge that something is the case. Verbal information involves facts or meaningfully connected prose recalled verbatim (e.g., words to a poem or the “Star Spangled Banner”). Schemas are forms of verbal information.

Cognitive strategies are executive control processes. They include information processing skills such as attending to new information, deciding to rehearse information, elaborating, using LTM retrieval strategies, and applying problem-solving strategies ( Chapter 7 ).

Motor skills are developed through gradual improvements in the quality (smoothness, timing) of movements attained through practice. Whereas intellectual skills can be acquired quickly, motor skills develop gradually with continued, deliberate practice (Ericsson, Krampe, & Tesch-Römer,  1993 ). Practice conditions differ: Intellectual skills are practiced with different examples; motor-skill practice involves repetition of the same muscular movements.

Attitudes are internal beliefs that influence actions and reflect characteristics such as generosity, honesty, and commitment to healthy living. Teachers can arrange conditions for learning intellectual skills, verbal information, cognitive strategies, and motor skills, but attitudes are learned indirectly through experiences and exposures to live and symbolic (televised, videotaped) models.

Learning Events.

The five types of learning outcomes differ in their conditions. Internal conditions are prerequisite skills and cognitive processing requirements; external conditions are environmental stimuli that support the learner’s cognitive processes. One must specify as completely as possible both types of conditions when designing instruction.

Internal conditions are learners’ current capabilities (knowledge in LTM). Instructional cues from teachers and materials activate relevant LTM knowledge (Gagné & Glaser,  1987 ). External conditions differ as a function of the learning outcome and the internal conditions. To teach students a classroom rule, a teacher might inform them of the rule and visually display it. To teach students a strategy for checking their comprehension, a teacher might demonstrate the strategy and give students practice and feedback on its effectiveness. Proficient readers are instructed differently from those with decoding problems. Each phase of instruction is subject to alteration as a function of learning outcomes and internal conditions.

Learning Hierarchies.

Learning hierarchies  are organized sets of intellectual skills. The highest element in a hierarchy is the target skill. To devise a hierarchy, one begins at the top and asks what skills the learner must perform prior to learning the target skill or what skills are immediate prerequisites for the target skill. Then one asks the same question for each prerequisite skill, continuing down the hierarchy until one arrives at the skills the learner can perform now (Dick & Carey,  1985 ; Merrill,  1987 ;  Figure 5.9 ).

Figure 5.9 Sample learning hierarchy.

Table 5.3 Gagné’s phases of learning.

Hierarchies are not linear orderings of skills. One often must apply two or more prerequisite skills to learn a higher-order skill with neither of the prerequisites dependent on the other. Nor are higher-order skills necessarily more difficult to learn than lower-order ones. Some prerequisites may be difficult to acquire; once learners have mastered the lower-order skills, learning a higher-order one may seem easier.

Phases of Learning.

Instruction is a set of external events designed to facilitate internal learning processes.  Table 5.3  shows the nine phases of learning grouped into the three categories (Gagné,  1985 ).

Preparation for learning includes introductory learning activities. During attending, learners focus on stimuli relevant to content to be learned (e.g., audiovisuals, written materials, teacher-modeled behaviors). The learner’s expectancy orients the learner to the goal (learn a motor skill, learn to reduce fractions). During retrieval of relevant information from LTM, learners activate the portions relevant to the topic studied (Gagné & Dick,  1983 ).

The main phases of learning are acquisition and performance. Selective perception means that the sensory registers recognize relevant stimulus features and transfer them to WM. Semantic encoding is the process whereby new knowledge is transferred to LTM. During retrieval and responding, learners retrieve new information from memory and make a response demonstrating learning.  Reinforcement refers to feedback that confirms the accuracy of a student’s response and provides corrective information as necessary.

Transfer of learning phases include cueing retrieval and generalizability. In cueing retrieval, learners receive cues signaling that previous knowledge is applicable in that situation. When solving word problems, for instance, a mathematics teacher might inform learners that their knowledge of right triangles is applicable. Generalizability is enhanced by providing learners the opportunity to practice skills with different content and under different circumstances (e.g., homework, spaced review sessions).

Table 5.4 Instructional events accompanying learning phases (Gagné).

These nine phases are equally applicable for the five types of learning outcomes. Gagné and Briggs ( 1979 ) specified types of instructional events that might accompany each phase ( Table 5.4 ). Instructional events enhancing each phase depend on the type of outcome. Instruction proceeds differently for intellectual skills than for verbal information.

One issue is that developing learning hierarchies can be difficult and time consuming. The process requires expertise in the content domain to determine the successive prerequisite skills—the scope and sequence of instruction. Even a seemingly simple skill may have a complex hierarchy if learners must master several prerequisites. For those skills with less well-defined structures (e.g., creative writing), developing a hierarchy may be difficult. Another issue is that the system allows for little learner control because it prescribes how learners should proceed, which could negatively affect motivation. Instructional technology that allows learners greater control over their activities may help override this possibility. These issues notwithstanding, the theory offers solid suggestions for ways to apply information processing principles to the design of instruction (Ertmer et al.,  2003 ).

Cognitive Load

Cognitive load  refers to the demands placed on the information processing system and in particular on WM (Paas, van Gog, & Sweller,  2010 ; Sweller,  2010 ; Winne & Nesbit,  2010 ). The capacity of WM is limited. Because information processing takes time and involves multiple cognitive processes, at any given time only a limited amount of information can be held in WM, transferred to LTM, rehearsed, and so forth.

Cognitive load theory takes these processing limitations into account in the design of instruction (DeLeeuw & Mayer,  2008 ; Schnotz & Kürschner,  2007 ; Sweller, van Merriënboer, & Paas,  1998 ). Cognitive load can be of two types.  Intrinsic cognitive load  refers to the demands placed on WM by the unalterable properties of the knowledge to be acquired.  Extrinsic (or extraneous) cognitive load  is a burden on WM caused by unnecessary content, distractions, or difficulties with the instructional presentation (Bruning et al.,  2011 ). Some researchers also speak of  germane cognitive load , which includes intrinsic load plus necessary extraneous load due to situational factors (e.g., monitoring of attention; Feldon,  2007 ).

For example, in learning key trigonometric relationships (e.g., sine, tangent), a certain cognitive load (intrinsic) is inherent in the material to be learned; namely, developing knowledge about the ratios of sides of a right triangle. Extraneous load would include information in instruction not relevant to the content to be learned, such as irrelevant features of pictures used. Teachers who give clear presentations help to minimize extraneous load and maximize germane load.

In similar fashion, Mayer ( 2012 ) distinguished three types of cognitive processing demands. Essential processing refers to cognitive processing necessary to mentally represent material in WM (similar to intrinsic load). Extraneous processing (similar to extrinsic load) refers to processing not necessary for learning and which wastes cognitive capacity. Generative processing is deeper cognitive processing that attempts to make sense of the material, such by organizing it and relating it to prior knowledge.

A key idea is that instructional methods should decrease extraneous cognitive load so that existing resources can be devoted to learning (van Merriënboer & Sweller,  2005 ). The use of  scaffolding  should be beneficial (van Merriënboer, Kirschner, & Kester,  2003 ). Initially the scaffold helps learners acquire skills that they would be unlikely to acquire without the assistance. The scaffolding helps to minimize the extrinsic load so learners can focus their resources on the intrinsic demands of the learning. As learners develop a schema to work with the information, the scaffold assistance can be phased out.

Another suggestion is to use simple-to-complex sequencing of material (van Merriënboer et al.,  2003 ), in line with Gagné’s theory. Complex learning is broken into simple parts that are acquired and combined into a larger sequence. This procedure minimizes extrinsic load, so learners can focus their cognitive resources on the learning at hand.

A third suggestion is to use authentic tasks in instruction. Reigeluth’s ( 1999 )  elaboration theory , for example, requires identifying conditions that simplify performance of the task and then beginning instruction with a simple but authentic case (e.g., one that might be encountered in the real world). Tasks that have real-world significance help to maximize germane load because they do not require learners to engage in extraneous processing to understand the context. It is more meaningful, for example, for students to determine the sine of the angle formed by joining a point 40 feet from the school’s flagpole to the top of the pole than it is to solve comparable trigonometric problems in a textbook.

These considerations also suggest the use of collaborative learning. As intrinsic cognitive load increases, learning becomes less effective and efficient (Kirschner, Paas, & Kirschner,  2009 ). With greater task complexity, dividing the cognitive processing demands across individuals reduces load on learners. These ideas fit well with the constructivist emphasis on peer collaboration ( Chapter 8 ). Some examples are provided in  Application 5.4 .

APPLICATION 5.4 Reducing Unnecessary Cognitive Load

Student learning will be best when instruction minimizes extraneous load and maximizes germane load. Ms. Watson, a high school English teacher, knows that locating symbolic elements in novels can prove taxing to many students. To help minimize extraneous load, she introduces only one symbolic element at a time, explains it, and asks students to try to find examples of it in only a few pages of the novel. By focusing their attention only on one element in a small subset of the novel, students do not feel overwhelmed by the demands of the task and their need to pay careful attention.

Ms. Anton, an elementary teacher, has students who have difficulty writing descriptive paragraphs. She breaks the task into parts so as to not impose a great extraneous load. First she has students write down what features of the object they want to describe in their paragraph. Then she asks them to write one sentence for each feature. When they are finished, she tells them to review their paragraphs and revise them as needed, making sure the paragraphs are clear and well organized.

Students in Professor Lauphar’s undergraduate educational psychology course have to do a group project where they design an ideal learning environment that addresses several concepts covered in the course (e.g., learning, motivation, assessment). Dr. Lauphar forms small groups of four students each and sets a timeline of when various aspects of the project are to be completed. Students meet in the groups and set their own timelines for when they will complete their research and re-convene as a group. By breaking the task into subparts and by reviewing the content areas over the course of the semester, students do not experience excessive extraneous load and can focus their attention and efforts on the immediate task at hand.

SUMMARY

Information processing theories focus on attention, perception, encoding, storage, and retrieval of knowledge. Information processing has been influenced by advances in communications, computer technology, and neuroscience.

Important historical influences on contemporary information processing views are verbal learning, Gestalt psychology, the two-store model, and levels of processing. Verbal learning researchers used serial learning, free recall, and paired-associate tasks. A number of important findings were obtained from verbal learning research. Free-recall studies showed that organization improves recall and that people impose their own organization when none is present. Gestalt theorists stressed the role of organization in perception and learning.

The two-store (dual) memory model was an early information processing model that posited stages of processing: sensory registers, perception, short-term memory, long-term memory. Levels of processing conceived of information processing in terms of depth, where information processed at deeper levels was more likely to be stored in memory and recalled.

A contemporary information processing model posits that information processing occurs in phases. Information enters through the sensory registers. Although there is a register for each sense, most research has been conducted on the visual and auditory registers. At any one time, only a limited amount of information can be attended to. Attention may act as a filter or a general limitation on capacity of the human system. Inputs attended to are perceived by being compared in WM with information in LTM.

When information enters WM, it can be retained through rehearsal and linked with related information in LTM. Information may be encoded for storage in LTM. Encoding is facilitated through organization, elaboration, and links with schemas. The central executive of WM controls its interface with perception and LTM.

Attention and perception processes involve critical features, templates, and prototypes. Whereas WM is limited in capacity and duration, LTM appears to be very large. The basic unit of knowledge is the proposition, and propositions are organized in networks. Major types of knowledge are declarative and procedural. Large bits of procedural knowledge may be organized in production systems. Networks further are linked in connectionist fashion through spreading activation.

Although much early research on information processing was basic in nature and conducted in experimental laboratories, researchers increasingly are conducting research in applied settings and especially on learning of academic content. Three instructional applications that reflect information processing principles involve advance organizers, the conditions of learning, and cognitive load.

Chapter 6 Information Processing Theory: Retrieval and Forgetting

Terrill Sharberg, an associate professor of education, is teaching an educational psychology course for graduate students. All of the students are educators—current or former teachers, administrators, or professional staff members. This week’s 3-hour class is devoted to remembering and forgetting. Several of the students have stories to tell.

Chapter 5  discussed how knowledge is encoded in  long-term memory  (LTM). The process is complex. It begins with learners attending to inputs so that these register, are perceived, and are processed in  working memory  (WM). The WM processing includes elaborating and organizing information and relating it to knowledge in LTM. Through this processing, new memory networks are created or existing ones are modified and enriched. When this happens, we say that learning (encoding) has occurred.

Practically speaking, however, such learning is useless if learners subsequently cannot access knowledge in LTM and thereby put it to further use. When this happens, we say that forgetting has occurred.  Forgetting  refers to the loss of information from memory or to the inability to retrieve information. Forgetting often has no serious consequences. When we find we have forgotten something, we may ask someone, look it up on the Internet, and so forth. Unless you plan to be a contestant on Jeopardy! or another quiz show that requires factual recall, the many things we forget in the course of a day pose no risk to life, liberty, or the pursuit of happiness!

But as the opening vignette shows, forgetting causes big problems in education. Teachers cannot move to more advanced content if students do not remember the basic prerequisite knowledge. Reviews and reteaching take up valuable class time. It is a frustrating experience for teachers, as well as for students who may become bored spending time on reviews. Learning should be exciting so that students and teachers remain motivated.

The focus of this chapter is on retrieval. Compared with only a few years ago, we now know much more about how knowledge is stored in memory and retrieved. We also know techniques that are effective in helping learners store knowledge in LTM such that it is easier to retrieve. Effective storage of knowledge facilitates not only retrieval but also transfer of knowledge to new situations and across time.

The next section discusses processes that individuals use to retrieve information from LTM. Language comprehension is used to exemplify these processes. The chapter then covers theories of forgetting and influences on forgetting. Given that forgetting occurs, relearning becomes necessary; this topic is addressed, along with how testing may affect learning and retrieval. Much of what we have discussed up to now has involved verbal memory, but visual memory is covered to include the benefits it provides for learning. The key educational topic of transfer is discussed including theoretical perspectives and types of transfer. Appropriate educational applications are described: encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.

· When you finish studying this chapter, you should be able to do the following:

· ■ Explain the information processes used to retrieve information from LTM including elaboration and spreading activation.

· ■ Describe encoding specificity and why it benefits retrieval.

· ■ Explain how language comprehension exemplifies information processes involved in storage and retrieval of knowledge.

· ■ Define interference and distinguish between retroactive and proactive interference.

· ■ Discuss an information processing perspective on forgetting.

· ■ Define visual memory and explain why it can promote learning.

· ■ Distinguish the different types of transfer, and explain why transfer is important for learning.

· ■ Discuss the components necessary for students to transfer use of learning strategies.

· ■ Explain the relevance of information processing principles in educational applications involving encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.

LONG-TERM MEMORY: RETRIEVAL

Retrieval Processes

Retrieval is a key aspect of information processing and actually can help promote learning (Karpicke & Grimaldi,  2012 ). This section discusses the processes involved in retrieval.

Retrieval Strategies.

What happens when a student is asked a question such as, “What does the vice president of the United States do in the Senate?” (see  Chapter 5 ). The question enters the student’s WM and is broken into propositions. The process by which this occurs has a neurological basis and is not well understood, but available evidence indicates that information activates associated information in memory networks through  spreading activation  to determine the answer to the question. If the answer is found, it is constructed into a sentence and verbalized to the questioner or into motor patterns to be written. If the activated propositions do not answer the query, activation spreads until the answer is located. When insufficient time is available for spreading activation to locate the answer, students may make an educated guess (Anderson,  1990 ).

Much cognitive processing occurs automatically. We routinely remember our home address and phone number, Social Security number, and close friends’ names. People are often unaware of all the steps taken to answer a question. However, when people must judge several activated propositions to determine whether the propositions properly answer the question, they are more aware of the process.

Because knowledge is encoded as propositions, retrieval proceeds even though the information to be retrieved does not exist in exact form in memory. If a teacher asks whether the vice president would vote on a bill when the initial vote was 51 for and 49 against, students could retrieve the proposition that the vice president votes only in the event of a tie. By implication, the vice president would not vote. Processing like this, which involves construction, takes longer than when a question requires information coded in memory in the same form, but students should respond correctly assuming they activate the relevant propositions in LTM. The same process is involved in transfer (discussed later in this chapter); for example, students learn a rule (e.g., the Pythagorean theorem in mathematics) and recall and apply it to solve problems they have never seen before.

Encoding Specificity.

Retrieval depends on the manner of encoding. According to the  encoding specificity hypothesis  (Brown & Craik,  2000 ; Thomson & Tulving,  1970 ), the manner in which knowledge is encoded determines which retrieval cues will effectively activate that knowledge. In this view, the best retrieval occurs when retrieval cues match those present during learning (Baddeley,  1998 ; Suprenant & Neath,  2009 ).

Some experimental evidence supports encoding specificity. When people are given category names while they are encoding specific instances of the categories, they recall the instances better if they are given the category names at recall than if not given the names (Matlin,  2009 ). A similar benefit is obtained if they learn words with associates and then are given the associate names at recall than if not given the associates. Brown ( 1968 ) gave students a partial list of U.S. states to read; others read no list. Subsequently all students recalled as many states as they could. Students who received the list recalled more of the states on the list and fewer states not on it.

Encoding specificity also includes context. In one study (Godden & Baddeley,  1975 ), scuba divers learned a word list either on shore or underwater. On a subsequent free recall task, learners recalled more words when they were in the same environment as the one in which they learned the words than when they were in the other environment.

Encoding specificity can be explained in terms of spreading activation among propositional networks. Cues associated with material to be learned are linked in LTM with the material at the time of encoding. During recall, presentation of these cues activates the relevant portions in LTM. In the absence of the same cues, recall depends on recalling individual propositions. Because the cues lead to spreading activation (not the individual propositions or concepts), recall is facilitated by presenting the same cues at encoding and recall. Other evidence suggests that retrieval is guided in part by expectancies about what information is needed and that people may distort inconsistent information to make it coincide with their expectations (Hirt, Erickson, & McDonald,  1993 ).

Retrieval of Declarative Knowledge.

Declarative knowledge often is processed automatically, but that is no guarantee that it will be integrated with relevant information in LTM and subsequently retrieved. We can see this inadequate retrieval in the scenario at the start of this chapter. Meaningfulness, elaboration, and organization enhance the potential for declarative information to be effectively processed and retrieved.  Application 6.1  provides some classroom examples.

APPLICATION 6.1 Organizing Information by Networks

Teachers enhance learning when they develop lessons to assist students to link new information with knowledge in memory. Information that is meaningful, elaborated, and organized is more readily integrated into LTM networks and retrieved.

A teacher planning a botany unit on the reproduction of different species of plants might start by reviewing common plant knowledge that students have stored in their memories (e.g., basic structure, conditions necessary for growth). As the teacher introduces new information, students examine familiar live plants that reproduce differently to make the experience more meaningful. Factual information to be learned can be elaborated by providing visual drawings and written details regarding the reproductive processes. For each live plant examined, students can organize the new information by creating outlines or charts to show the means of reproduction.

An art teacher planning a design unit might start by reviewing the various elements of color, shape, and texture. As the teacher introduces new techniques related to placement, combination of the various elements, and balance as it relates to the whole composition, manipulatives of various shapes, colors, and textures are provided for each student to use in creating different styles. The students can use the manipulatives to organize the elements and media they want to include in each of their design compositions.

Meaningfulness improves retrieval. Nonmeaningful information will not activate information in LTM and will be lost unless students rehearse it repeatedly until it becomes established in LTM, perhaps by forming a new propositional network. One also can connect the sounds of new information, which are devoid of meaning, to other similar sounds. The word constitution, for example, may be linked phonetically with other uses of the word stored in learners’ memories (e.g., Constitution Avenue).

Meaningful information is more likely to be retained because it easily connects to propositional networks. In the opening scenario in  Chapter 5 , one suggestion offered is to relate algebraic variables to tangible objects—things that students understand—to give the algebraic notation some meaning. Meaningfulness not only promotes learning, but it also saves time. Propositions in WM take time to process; Simon ( 1974 ) estimated that each new piece of information takes 10 seconds to encode, which means that only six new pieces of information can be processed in a minute. Even when information is meaningful, much knowledge is lost before it can be encoded. Although every piece of incoming information is not important and some loss usually does not impair learning, students typically retain little information even under the best circumstances.

When we elaborate we add to information being learned with examples, details, inferences, or anything that serves to link new and old information. A learner might elaborate the role of the vice president in the Senate by thinking through the roll call and, when there is a tie, having the vice president vote.

Elaboration facilitates learning because it is a form of rehearsal: By keeping information active in WM, elaboration increases the likelihood that information will be permanently stored in LTM. This facilitates retrieval, as does the fact that elaboration establishes links between old and new information. Students who elaborate the role of the vice president in the Senate link this new information with what they know about the Senate and the vice president. Well-linked information in LTM, often stored as schemas, is easier to recall than poorly linked information (Stein, Littlefield, Bransford, & Persampieri,  1984 ; Surprenant & Neath,  2009 ).

Although elaboration promotes storage and retrieval, it also takes time. Comprehending sentences requiring elaboration takes longer than sentences not requiring elaboration (Haviland & Clark,  1974 ). For example, the following sentences require drawing an inference that Marge took her credit card to the grocery store: “Marge went to the grocery store,” and “Marge charged her groceries.” The link is clarified in the following sentences: “Marge took her credit card to the grocery store,” and “Marge used her credit card to pay for her groceries.” Making explicit links between adjoining propositions assists their encoding and retention.

An important aspect of learning is deciding on the importance of information. Not all learned information needs to be elaborated. Comprehension is aided when students elaborate only the most important aspects of text (Reder,  1979 ). Elaboration aids retrieval by providing alternate paths along which activation can spread, so that if one path is blocked, others are available (Anderson,  1990 ,  2000 ). Elaboration also provides additional information from which answers can be constructed (Reder,  1982 ), such as when students must answer questions with information in a different form from that of the learned material.

In general, almost any type of elaboration assists encoding and retrieval; however, some elaborations are more effective than others. Activities such as taking notes and asking how new information relates to what one knows build propositional networks. Effective elaborations link propositions and stimulate accurate recall. Elaborations not linked well to the content do not aid recall (Mayer,  1984 ).

Organization takes place by breaking information into parts and specifying relationships between parts. In studying U.S. government, organization might involve breaking government into three branches (executive, legislative, judicial), breaking each of these into subparts (e.g., functions, agencies), and so on. Older students employ organization more often, but elementary children are capable of using organizational principles (Meece,  2002 ). Children studying leaves may organize them by size, shape, and edge pattern.

Organization improves retrieval by linking relevant information; when retrieval is cued, spreading activation accesses the relevant propositions in LTM. Teachers routinely organize material, but student-generated organization is also effective for retrieval. Instruction on organizational principles assists learning. Consider a schema for understanding stories with four major attributes: setting, theme, plot, and resolution (Rumelhart,  1977 ). The setting (“Once upon a time…”) places the action in a context. The theme is then introduced, which consists of characters who have certain experiences and goals. The plot traces the actions of the characters to attain their goals. The resolution describes how the goal is reached or how the characters adjust to not attaining the goal. By describing and exemplifying these phases of a story, teachers help students learn to identify them on their own.

Retrieval of Procedural Knowledge.

Retrieval of procedural knowledge is similar to that of declarative knowledge. Retrieval cues trigger associations in memory, and the process of spreading activation activates and recalls relevant knowledge. Thus, if students are told to perform a procedure in chemistry laboratory, they will cue that production in memory, retrieve it, and implement it.

When declarative and procedural knowledge interact, retrieval of both is necessary. While adding fractions, students use procedures (i.e., convert fractions to their lowest common denominator, add numerators) and declarative knowledge (addition facts). During reading comprehension, some processes operate as procedures (e.g., decoding, monitoring comprehension), whereas others involve only declarative knowledge (e.g., word meanings, functions of punctuation marks). People typically employ procedures to acquire declarative knowledge, such as mnemonic techniques to remember declarative knowledge (see  Chapter 10 ). Having declarative information is typically a prerequisite for successfully implementing procedures. To solve for roots using the quadratic formula, students must know multiplication facts.

Declarative and procedural knowledge vary tremendously in scope. Individuals possess declarative knowledge about the world, themselves, and others; they understand procedures for accomplishing diverse tasks. Declarative and procedural knowledge are different in that procedures transform information. Such declarative statements as “2 × 2 = 4” and “Uncle Fred smokes smelly cigars” change nothing, but applying the long-division algorithm to a problem changes an unsolved problem into a solved one.

Another difference is in speed of processing. Retrieval of declarative knowledge often is slow and conscious. Even assuming people know the answer to a question, they may have to think for some time to answer it. For example, consider the time needed to answer “Who was the U.S. president in 1867?” (Andrew Johnson). In contrast, once procedural knowledge is established in memory, it is retrieved quickly and often automatically. Skilled readers decode printed text automatically; they do not have to consciously reflect on what they are doing. Processing speed distinguishes skilled from poor readers (de Jong,  1998 ). Once we learn how to multiply, we do not have to think about what steps to follow to solve problems.

The differences in declarative and procedural knowledge have implications for teaching and learning. Students may have difficulty with a particular content area because they lack domain-specific declarative knowledge or because they do not understand the prerequisite procedures. Discovering which is deficient is a necessary first step for planning remedial instruction. Not only do deficiencies hinder learning, they also produce low self-efficacy ( Chapter 4 ). Students who understand how to divide but do not know multiplication facts become demoralized when they consistently arrive at wrong answers.

Language Comprehension

An application illustrating storage and retrieval of information in LTM is language comprehension (Carpenter, Miyake, & Just,  1995 ; Corballis,  2006 ; Matlin,  2009 ). Language comprehension is highly relevant to school learning and especially in light of the increasing number of students whose native language is not English (Fillmore & Valadez,  1986 ; Hancock,  2001 ; Padilla,  2006 ).

Comprehending spoken and written language represents a problem-solving process involving domain-specific declarative and procedural knowledge (Anderson,  1990 ). Language comprehension has three major components: perception, parsing, and utilization.  Perception  involves attending to and recognizing an input; sound patterns are translated into words in WM.  Parsing  means mentally dividing the sound patterns into units of meaning.  Utilization  refers to the disposition of the parsed mental representation: storing it in LTM if it is a learning task, giving an answer if it is a question, asking a question if it is not comprehended, and so forth. This section covers parsing and utilization; perception was discussed in  Chapter 5  ( Application 6.2 ).

Parsing.

Linguistic research shows that people understand the grammatical rules of their language, even though they usually cannot verbalize them (Clark & Clark,  1977 ). Beginning with the work of Chomsky ( 1957 ), researchers have investigated the role of  deep structures  containing prototypical representations of language structure. The English language contains a deep structure for the pattern “noun 1–verb–noun 2,” which allows us to recognize these patterns in speech and interpret them as “noun 1 did verb to noun 2.” Deep structures may be represented in LTM as  productions . Chomsky postulated that the capacity for acquiring deep structures is innately human, although which structures are acquired depends on the language of one’s culture.

Parsing includes more than just fitting language into productions. When people are exposed to language, they construct a mental representation of the situation. They recall from LTM propositional knowledge about the context into which they integrate new knowledge. A central point is that all communication is incomplete. Speakers do not provide all information relevant to the topic being discussed. Rather, they omit the information listeners are most likely to know (Clark & Clark,  1977 ). For example, suppose Sam meets Kira and Kira remarks, “You won’t believe what happened to me at the concert!” Sam is most likely to activate propositional knowledge in LTM about concerts. Then Kira says, “As I was locating my seat…” To comprehend this statement, Sam must know that one purchases a ticket with an assigned seat. Kira did not tell Sam these things because she assumed he knew them.

APPLICATION 6.2 Language Comprehension

Students presented with confusing or vague information may misconstrue it or relate it to the wrong context. Teachers need to present clear and concise information and ensure that students have adequate background information to build networks and schemata.

Mrs. Lineahan plans to present a social studies unit comparing city life with life in the country, but she is afraid that most of her fourth-grade students will have difficulty comprehending the unit because they never have seen a farm. They may never have heard words such as silo, milking, sow, and livestock. Mrs. Lineahan can produce better student understanding by providing farm-related experiences: take a field trip to a farm; show video clips illustrating farm life; and bring in farm materials such as seeds and plants. As students become familiar with farms, they will be better able to comprehend spoken and written communication about farms.

Young children may have difficulty following directions in preschool and kindergarten. Their limited use and understanding of language may cause them to interpret certain words or phrases differently than intended. For instance, if a teacher said to a small group of children playing in a “dress-up” center, “Let’s get things tied up so we can work on our next activity,” the teacher might return to find children tying clothes together instead of cleaning up! Or a teacher might say, “Make sure you color this whole page,” to children working with crayons. Later the teacher may discover that some children took a single crayon and colored the entire page from top to bottom instead of using various colors to color the items on the page. Teachers must explain, demonstrate, and model what they want children to do. Then they can ask the children to repeat in their own words what they think they are supposed to do.

Effective parsing requires knowledge and inferences (Resnick,  1985 ). When exposed to verbal communication, individuals access information from LTM about the situation. This information exists in LTM as  propositional networks  hierarchically organized as schemas. Networks allow people to understand incomplete communications. Consider the following sentence: “I went to the grocery store and saved five dollars with coupons.” Knowledge that people buy merchandise in grocery stores and that they can redeem coupons to reduce costs enables listeners to comprehend this sentence. The missing information is filled in with knowledge in memory.

People often misconstrue communications because they construct missing information with the wrong context. When given a vague passage about four friends getting together for an evening, music students interpreted it as a description of playing music, whereas physical education students described it as an evening of playing cards (Anderson, Reynolds, Schallert, & Goetz,  1977 ). The interpretative schemas salient in people’s minds are used to comprehend problematic passages. As with many other linguistic skills, interpretations of communications become more reliable with development as children realize both the literal meaning of a message and its intent (Beal & Belgrad,  1990 ).

That spoken language is incomplete can be shown by decomposing communications into propositions and identifying how propositions are linked. Consider this example (Kintsch,  1979 ):

·  The Swazi tribe was at war with a neighboring tribe because of a dispute over some cattle. Among the warriors were two unmarried men named Kakra and his younger brother Gum. Kakra was killed in battle.

Although this passage seems straightforward, analysis reveals the following 11 distinct propositions:

· 1. The Swazi tribe was at war.

· 2. The war was with a neighboring tribe.

· 3. The war had a cause.

· 4. The cause was a dispute over some cattle.

· 5. Warriors were involved.

· 6. The warriors were two men.

· 7. The men were unmarried.

· 8. The men were named Kakra and Gum.

· 9. Gum was the younger brother of Kakra.

· 10. Kakra was killed.

· 11. The killing occurred during battle.

Even this propositional analysis is incomplete. Propositions 1 through 4 link together, as do Propositions 5 through 11, but a gap occurs between 4 and 5. To supply the missing link, one might have to change Proposition 5 to “The dispute involved warriors.”

Kintsch and van Dijk ( 1978 ) showed that features of communication influence comprehension. Comprehension becomes more difficult when more links are missing and when propositions are further apart (in the sense of requiring inferences to fill in the gaps). When much material has to be inferred, WM becomes overloaded and comprehension suffers.

Just and Carpenter ( 1992 ) formulated a capacity theory of language comprehension, which postulates that comprehension depends on WM capacity, in which individuals differ. Elements of language (e.g., words, phrases) become activated in WM and are operated on by other processes. If the total amount of activation available to the system is less than the amount required to perform a comprehension task, then cognitive load is high (see  Chapter 5 ), and some of the activation maintaining older elements will be lost (Carpenter et al.,  1995 ). Elements comprehended at the start of a lengthy sentence may be lost by the end. Production-system rules presumably govern activation and the linking of elements in WM.

We see the application of this model in parsing of ambiguous sentences or phrases (e.g., “The soldiers warned about the dangers…”; MacDonald, Just, & Carpenter,  1992 ). Although alternative interpretations of such constructions initially may be activated, the duration of maintaining them depends on WM capacity. Persons with large WM capacities maintain the interpretations for quite a while, whereas those with smaller capacities typically maintain only the most likely (although not necessarily correct) interpretation. With increased exposure to the context, comprehenders can decide which interpretation is correct, and such identification is more reliable for persons with large WM capacities who still have the alternative interpretations in WM (Carpenter et al.,  1995 ; King & Just,  1991 ).

In building representations, people include important information and omit details (Resnick,  1985 ). These gist representations include propositions most germane to comprehension. Listeners’ ability to make sense of a text depends on what they know about the topic (Chiesi, Spilich, & Voss,  1979 ; Spilich, Vesonder, Chiesi, & Voss,  1979 ). When the appropriate network or schema exists in listeners’ memories, they employ a production that extracts the most central information to fill the slots in the schema. Comprehension proceeds slowly when a network must be constructed because it does not exist in LTM.

Stories exemplify how schemas are employed. Stories have a prototypical schema that includes setting, initiating events, internal responses of characters, goals, attempts to attain goals, outcomes, and reactions (Black,  1984 ; Rumelhart,  1975 ,  1977 ; Stein & Trabasso,  1982 ). When hearing a story, people construct a mental model of the situation by recalling the story schema and gradually fitting information into it (Bower & Morrow,  1990 ; Surprenant & Neath,  2009 ). Some categories (e.g., initiating events, goal attempts, consequences) are nearly always included, but others (internal responses of characters) may be omitted (Mandler,  1978 ; Stein & Glenn,  1979 ). Comprehension proceeds quicker when schemas are easily activated. People recall stories better when events are presented in the expected order (i.e., chronological) rather than in a nonstandard order (i.e., flashback). When a schema is well established, people rapidly integrate information into it. Research shows that early home literacy experiences that include exposure to books relate positively to the development of listening comprehension (Sénéchal & LeFevre,  2002 ).

Utilization.

Utilization  refers to what people do with the communications they receive. For example, if the communicator asks a question, listeners retrieve information from LTM to answer it. In a classroom, students link the communication with related information in LTM.

To use sentences properly, as speakers intend them, listeners must encode three pieces of information: speech act, propositional content, and thematic content. A speech act is the speaker’s purpose in uttering the communication, or what the speaker is trying to accomplish with the utterance (Austin,  1962 ; Searle,  1969 ). Speakers may be conveying information to listeners, commanding them to do something, requesting information from them, promising them something, and so on. Propositional content is information that can be judged true or false. Thematic content refers to the context in which the utterance is made. Speakers make assumptions about what listeners know. On hearing an utterance, listeners infer information not explicitly stated but germane to how it is used. The speech act and propositional and thematic contents are most likely encoded with productions.

As an example of this process, assume that Ms. Gravitas is discussing history and questioning students about text material. She might ask, “What was Churchill’s position during World War II?” The speech act is a request and is signaled by the sentence beginning with a WH word (e.g., who, which, where, when, and why). The propositional content refers to Churchill’s position during World War II; it might be represented in memory as follows: Churchill–Prime Minister–Great Britain–World War II. The thematic content refers to what the teacher left unsaid; the teacher assumes students have heard of Churchill and World War II. Thematic content also includes the classroom question-and-answer format. The students understand that they will be asked questions.

Of special importance is how students encode assertions. When teachers utter an assertion, they are conveying to students they believe the stated proposition is true. If Ms. Gravitas said, “Churchill was the prime minister of Great Britain during World War II,” she is conveying her belief that this assertion is true. Students record the assertion with related information in LTM.

Speakers may facilitate the process whereby people relate new assertions with information in LTM by employing the given-new contract (Clark & Haviland,  1977 ), a type of implicit understanding. Given information should be readily identifiable, and new information should be unknown to the listener. We might think of the given-new contract as a production. In integrating information into memory, listeners identify given information, access it in LTM, and relate new information to it (i.e., store it in the appropriate “slot” in the network). For the given-new contract to enhance utilization, given information must be readily identified by listeners. When given information is not readily available because it is not in listeners’ memories or has not been accessed in a long time, using the given-new production is difficult.

Although language comprehension is often overlooked in school in favor of reading and writing, it is a central component of information processing and literacy. Educators lament the poor listening and speaking skills of students, and these are valued attributes of leaders. Habit 5 of Covey’s ( 1989 ) Seven Habits of Highly Effective People is “Seek first to understand, then to be understood,” which emphasizes listening first and then speaking. Listening is intimately linked with high achievement. A student who is a good listener is rarely a poor reader. Among college students, measures of listening comprehension may be indistinguishable from those of reading comprehension (Miller,  1988 ).

FORGETTING

It was noted earlier that  forgetting  involves the loss of knowledge from memory or the inability to retrieve knowledge. Researchers disagree about whether information is lost from memory or whether it still is present but cannot be retrieved because it has been distorted, the retrieval cues are inadequate, or other information is interfering with its recall. Forgetting has been studied experimentally since the time of Ebbinghaus ( Chapter 1 ). Before presenting an information processing perspective on forgetting that involves interference and decay, some historical work on interference is discussed.

Table 6.1 Interference and forgetting.

Note: Each group learns the task to some criterion of mastery. The “—” indicates a period of time in which the group is engaged in another task that prevents rehearsal but does not interfere with the original learning. Interference is demonstrated if group 2 outperforms group 1 on the test.

Interference Theory

One of the contributions of verbal learning research ( Chapter 5 ) was the interference theory of forgetting. According to this theory, learned associations are never completely forgotten. Forgetting results from competing associations that lower the probability of the correct association being recalled; that is, other material becomes associated with the original stimulus (Postman,  1961 ). The problem lies in retrieving information from memory rather than in memory itself.

Two types of interference were experimentally identified ( Table 6.1 ).  Retroactive interference  occurs when new verbal associations make remembering prior associations difficult.  Proactive interference refers to older associations that make newer learning more difficult.

To demonstrate retroactive interference, an experimenter might ask two groups of individuals to learn Word List A. Group 1 then learns Word List B, while group 2 engages in a competing activity to prevent rehearsal of List A. Both groups then attempt to recall List A. Retroactive interference occurs if the recall of Group 2 is better than that of Group 1. For proactive interference, Group 1 learns List A while Group 2 does nothing. Both groups then learn List B and attempt to recall List B. Proactive interference occurs if the recall of Group 2 surpasses that of Group 1.

Retroactive and proactive interference occur often in school. Retroactive interference is seen among students who learn words with regular spellings and then learn words that are exceptions to spelling rules. If, after some time, they are tested on the original words, they might alter the spellings to those of the exceptions. Proactive interference is evident among students taught first to multiply and then to divide fractions. When subsequently tested on division, they may simply multiply without first inverting the second fraction. Developmental research shows that proactive interference decreases between the ages of 4 and 13 (Kail,  2002 ).  Application 6.3  offers suggestions for dealing with interference.

Interference theory represented an important step in specifying memory processes. Early theories of learning postulated that learned connections leave a memory “trace” that weakens and decays with nonuse. Skinner ( 1953 ;  Chapter 3 ) did not postulate an internal memory trace but suggested that forgetting results from lack of opportunity to respond due to the stimulus being absent for some time. Each of these views has shortcomings. Although some decay may occur (discussed later), the memory trace notion is vague and difficult to verify experimentally. The nonuse position holds at times, but exceptions do exist; for example, being able to recall information after many years of nonuse (e.g., names of some elementary school teachers) is not unusual. Interference theory surmounts these problems by postulating how information in memory becomes confused with other information. It also specifies a research model for investigating these processes.

APPLICATION 6.3 Interference in Teaching and Learning

Proactive and retroactive interference occur often in teaching and learning. Teachers cannot completely eliminate interference, but they can minimize its effects by recognizing areas in the curriculum that easily lend themselves to interference. For example, students learn to subtract without regrouping and then to subtract with regrouping. Ms. Hastings often finds that when she gives her third-grade students review problems requiring regrouping, some students do not regroup. To minimize interference, she teaches students the underlying rules and principles and has them practice applying the skills in different contexts. She points out similarities and differences between the two types of problems and teaches students how to decide whether regrouping is necessary. Frequent reviews help to minimize interference.

When spelling words are introduced at the primary level, words often are grouped by phonetic similarities (e.g., crate, slate, date, state, mate, late); however, when children learn certain spelling patterns, it may confuse them as they encounter other words (e.g., weight or wait rather than wate; freight rather than frate). Ms. Hastings provides additional instruction regarding other spellings for the same sounds and exceptions to phonetic rules along with periodic reviews over time. This reinforcement should help alleviate confusion and interference among students.

Postman and Stark ( 1969 ) suggested that suppression, rather than interference, causes forgetting. Participants in learning experiments hold in active memory material they believe they will need to recall later. Those who learn List A and then are given List B are apt to suppress their responses to the words on List A. Such suppressions would last while they are learning List B and for a while thereafter. In support of this point, the typical retroactive interference paradigm produces little forgetting when learners are given a recognition test on the original Word List A rather than asked to recall the words.

Tulving ( 1974 ) postulated that forgetting represents inaccessibility of information due to improper retrieval cues. Information in memory does not decay, become confused, or get lost. Rather, the memory trace is intact but cannot be accessed. Memory of information depends on the trace being intact and on having adequate retrieval cues. Perhaps you cannot remember your home phone number from when you were a child. You may not have forgotten it; the memory is submerged because your current environment is different from that of years ago, and the cues associated with your old home phone number—your house, street, neighborhood—are absent. This principle of cue-dependent  forgetting  also is compatible with the common finding that people perform better on recognition than on recall tests. In the cue-dependent view, they should perform better in recognition tests because more retrieval cues are provided; in recall tests, they must supply their own cues.

Later research on interference suggested that interference occurs (e.g., people confuse elements) when the same cognitive schema or plan is used on multiple occasions (Thorndyke & Hayes-Roth,  1979 ; Underwood,  1983 ). Interference theory continues to provide a viable framework for investigating forgetting (Brown, Neath, & Chater,  2007 ; Oberauer & Lewandowsky,  2008 ).

Information Processing

From an information processing perspective,  interference  refers to a blockage of the spread of activation across memory networks (Anderson,  1990 ). For various reasons, when people attempt to access information in memory, the activation process is thwarted. Although the mechanism blocking activation is not completely understood, theory and research suggest various causes of interference.

One factor that can affect whether structures are activated is the strength of original encoding. Information that originally is strongly encoded through frequent rehearsal or extensive elaboration is more likely to be accessed than information that originally is weakly encoded.

A second factor is the number of alternative network paths down which activation can spread (Anderson,  1990 ). Information that can be accessed via many routes is more likely to be remembered than information that is only accessible via fewer paths. For example, if I want to remember the name of Aunt Frieda’s parakeet (Mr. T), I should associate that with many cues, such as my friend Mr. Thomas, the fact that when Mr. T spreads his wings it makes the letter T, and the idea that his constant chirping taxes my tolerance. Then, when I attempt to recall the name of the parakeet I can access it via my memory networks for Aunt Frieda and for parakeets. If these fail, then I still have available the networks for my friends, the letter T, and things that tax my tolerance. In contrast, if I associate only the name “Mr. T” with the bird, then the number of alternative paths available for access is fewer and the likelihood of interference is greater.

A third factor is the amount of distortion or merging of information. We have discussed the memory benefits of organizing, elaborating, and making information meaningful by relating it to what we know. Whenever we engage in these practices, we change the nature of information, and in some cases we merge it with other information or subsume it under more general categories. Such merging and subsumption facilitate  meaningful reception learning  (Ausubel,  1963 ,  1968 ; see  Chapter 5 ). Sometimes, however, such distortion and merging may cause interference and make recall more difficult than if information is remembered on its own.

Interference is an important cause of forgetting, but it is unlikely that it is the only one (Anderson,  1990 ). It appears that some information in LTM decays systematically with the passage of time and independently of any interference. Wickelgren ( 1979 ) traced systematic decay of information in time intervals ranging from 1 minute to 2 weeks. Information decays rapidly at first with decay gradually tapering off. Researchers find little forgetting after 2 weeks. However, the best evidence for decay is found in memories that are time bound; namely, sensory memory and WM (Surprenant & Neath,  2009 )

The position that forgetting occurs because of decay is difficult to affirm or refute. Explanations given for decay often are vague (Surprenant & Neath,  2009 ). Failure to recall even with extensive cuing does not unequivocally support a decay position because it still is possible that the appropriate memory networks were not activated. Similarly, the fact that the decay position posits no psychological processes responsible for forgetting (rather only the passage of time) does not refute the position. Memory traces include both perceptual features and reactions to the experiences (Estes,  1997 ). Decay or changes in one or both cause forgetting and memory distortions. Furthermore, the decay process may be neurological (Anderson,  1990 ). Synapses deteriorate with lack of use in the same way that muscles do ( Chapter 2 ).

Decay is commonly cited as a reason for forgetting (Nairne,  2002 ). You may have learned French in high school but now some years later cannot recall many vocabulary words. You might explain that as, “I haven’t used it for so long that I’ve forgotten it.” And forgetting is beneficial. Were we to remember everything we have ever learned, our memories would be so overcrowded that new learning would be very difficult. Forgetting is facilitative when it rids us of knowledge that we have not used and thus may not be important, analogous to your discarding things that you no longer need. Forgetting leads people to act, think, judge, and feel differently than they would in the absence of forgetting (Riccio, Rabinowitz, & Axelrod,  1994 ). Forgetting has profound effects on teaching and learning ( Application 6.4 ).

APPLICATION 6.4 Minimizing Forgetting of Academic Learning

Forgetting is a problem when learned knowledge is needed for new learning. To help children retain important information and skills, teachers might do the following:

· ■ Periodically review important information and skills during classroom activities.

· ■ Assign class work and homework that reinforce previously learned material and skills.

· ■ Send home fun learning packets during long vacation breaks that will reinforce various information and skills acquired.

· ■ When introducing a new lesson or unit, review previously learned material that is needed for mastering the new material.

When Mrs. Baitwick-Smith introduces long division, some third graders have forgotten how to regroup in subtraction, which can slow the new learning. She spends a couple of days reviewing subtraction—especially problems requiring regrouping—as well as drilling the students on multiplication and simple division facts. She also gives homework that reinforces the same skills.

Ms. Zhang, a physical education teacher, is teaching a basketball unit over several days. At the start of each class, she reviews the skills taught in the previous class before she introduces the new skill. Periodically she spends an entire class period reviewing all the skills (e.g., dribbling, passing, shooting, playing defense) that the students have been working on up to that point. Remedial instruction is necessary when students forget some of these skills so that they will be able to play well once Ms. Zhang begins to organize games.

In Professor Astoolak’s graduate seminar, the students have been assigned an application paper that focuses on motivation techniques. During the semester, she introduced various motivational theories. Many of the students have forgotten some of these. To help the students prepare for writing their papers, she spends part of one class period reviewing the major motivation theories. Then she divides students into small groups and has each group write a brief summary of one of the theories with some classroom applications. After working in small groups, each group shares its findings with the entire class.

RELEARNING

Memory Savings

Relearning  is learning material for the second or subsequent time after it previously had been learned (i.e., had satisfied the criteria of learning as stated in  Chapter 1 ). Relearning is a common phenomenon and occurs daily for all of us. The opening vignette exemplifies relearning that occurs in school settings.

But relearning is more than just a common human activity, it also strikes to the heart of the issue about whether knowledge, once encoded in LTM, is there permanently or whether it can be lost. Recall from  Chapter 1  the research by Ebbinghaus on memory. He relearned material some time after the original learning and calculated the savings score, or the amount of time or number of trials necessary to relearn as a percentage of the amount of time or number of trials necessary for original learning. The result that relearning is easier than new learning has been obtained in other research studies (Bruning, Schraw, & Norby,  2011 ).

Since relearning is easier than new learning, it suggests that at least some knowledge in LTM may not be permanently lost. Forgetting is said to occur when knowledge cannot be retrieved, perhaps because of inadequate retrieval cues, retrieval conditions not matching those of original learning, and so forth. Relearning research suggests that we may not forget but rather retain in LTM more knowledge than we can recall, recognize, or otherwise retrieve.

From the theoretical perspective of information processing, it is not clear why relearning is more efficient than new learning. It may be that memory network traces are retained, so that when people relearn they reconstruct these memories. Neuroscience research ( Chapter 2 ) shows that networks respond to use, so when people do not use them they become weakened but not necessarily lost (Wolfe,  2010 ).

As with new learning, relearning proceeds better with distributed practice (regular shorter sessions) than with massed practice (irregular, more intense sessions; Bruning et al.,  2011 ). Perhaps the distributing of relearning allows memory networks to strengthen in such a way that they become established better.

Effect of Testing

Another factor that seems to affect relearning is testing. The role of testing in accountability was discussed in  Chapter 1 . There is much pressure on schools today to ensure that students learn requisite skills and meet learning standards and outcomes. This emphasis can create a negative view of testing among educators, parents, and students.

A  testing effect  occurs when taking tests or quizzes enhances learning and retention such that scores on the final test are higher than if prior testing had not occurred (Bruning et al.,  2011 ). This effect suggests that some learning occurs while students are being tested, presumably because they recall and rehearse material and relate it in new ways to other knowledge. What is also interesting, however, is that taking a test on material can have a stronger effect on retention than spending the same amount of time restudying material (Bruning et al.,  2011 ). Roediger and Karpicke ( 2006 ) found that on a test a week after learning, students who studied and were tested on the material during original learning outperformed those who only had studied the material.

Being tested while one is learning forces one to retrieve material. It may be that the testing requires learners to organize and elaborate material better, both of which lead to better long-term retention and relearning. Also, the retrieval practiced during learning is done under similar conditions as that done during subsequent testing, so we should expect good transfer from the original learning context to the later testing one. Transfer is discussed later in this chapter.

This benefit should not be construed as an argument for more testing in schools. But educators who are aware of the potential advantage can design curricula to use testing not just for accountability but also as a means to promote learning. The judicious use of quizzes and tests may help alleviate some of the need for reviews lamented by the educators in the opening vignette.

VISUAL MEMORY

Chapters 5  and  6  have focused primarily on verbal memory—the memory of words and meanings. But another type of memory used commonly in learning is visual memory (Matlin,  2009 ). In fact, people often tend to remember information better in visual rather than in verbal form, and memory is enhanced when information is presented in both forms (Sadoski & Paivio,  2001 ).

Visual memory (or visual imagery or  mental imagery ) refers to mental representations of visual/spatial knowledge including physical properties of the objects or events represented. This section discusses how knowledge is represented visually and individual differences in visual memory capabilities.

Representation of Visual Information

Visual stimuli that are attended to are held briefly in veridical (true) form in the sensory register and then are transferred to WM. Recall from  Chapter 5  that in WM the visuo-spatial sketchpad serves to set up and manipulate visual images (Baddeley,  1998 ,  2012 ). The WM representation appears to preserve some of the physical attributes of the stimulus it represents. Images are analog representations that are similar but not identical to their referents.

Visual memory has been valued as far back as the time of the ancient Greeks. Plato felt that thoughts and perceptions are impressed on the mind as on a block of wax and are remembered as long as the images last (Paivio,  1970 ). Simonides, a Greek poet, believed that images are associative mediators. He devised the  method of loci  as a memory aid ( Chapter 10 ). In this method, information to be remembered is paired with locations in a familiar setting.

Visual imagery also has been influential in discoveries. Shepard ( 1978 ) described Einstein’s Gedanken experiment that marked the beginning of the relativistic reformulation of electromagnetic theory. Einstein imagined himself traveling with a beam of light (186,000 miles per second), and what he saw corresponded neither to light nor to anything described by Maxwell’s equations in classical electromagnetic theory. Einstein reported that he typically thought in terms of images and only reproduced his thoughts in words and mathematical equations once he conceptualized the situation visually. The German chemist Kekulé supposedly had a dream in which he visualized the structure of benzene, and Watson and Crick apparently used mental rotation to break the genetic code.

In contrast to images, propositions are discrete representations of meaning not resembling their referents in structure. The expression “New York City” no more resembles the actual city than virtually any three words picked at random from a dictionary. An image of New York City containing skyscrapers, stores, people, and traffic is more similar in structure to its referent. The same contrast is evident for events. Compare the sentence, “The black dog ran across the lawn,” with an image of this scene.

Visual memory is a controversial topic (Matlin,  2009 ). A central issue is how closely visual images resemble actual pictures: Do they contain the same details as pictures or are they fuzzy pictures portraying only highlights? The visual pattern of a stimulus is perceived when its features are linked to a LTM representation. This implies that images can only be as clear as the LTM representations (Pylyshyn,  1973 ). To the extent that images are the products of people’s perceptions, images are likely to be incomplete representations of stimuli. In fact, people construct images in memory and then reconstruct them during retrieval (Surprenant & Neath,  2009 ), both of which cause distortion.

Support for the idea that people use visual imagery to represent spatial knowledge comes from studies where participants were shown pairs of two-dimensional pictures, each of which portrayed a three-dimensional object (Cooper & Shepard,  1973 ; Shepard & Cooper,  1983 ). The task was to determine if the two pictures in each pair portrayed the same object. The solution strategy involved mentally rotating one object in each pair until it matched the other object or until the individual decided that no amount of rotation would yield an identical object. Reaction times were a direct function of the number of mental rotations needed. Although these and other data suggest that people employ images to represent knowledge, they do not directly address the issue of how closely images correspond to actual objects.

To the extent that students use imagery to represent spatial and visual knowledge, imagery is germane to educational content involving concrete objects. When teaching a unit about different types of rock formations (e.g., mountains, plateaus, ridges), an instructor could show pictures of the various formations and ask students to imagine them. In geometry, imagery could be employed when dealing with mental rotations. Pictorial illustrations improve students’ learning from texts (Carney & Levin,  2002 ; see  Application 6.5  for more examples).

APPLICATION 6.5 Using Visual Memory in Classrooms

Visual memory can improve student learning. One application involves instructing students on three-dimensional figures (e.g., cubes, spheres, cones), including calculating their volumes. Verbal descriptors and two-dimensional diagrams are also used, but actual models of the figures greatly enhance teaching effectiveness. Allowing students to hold the shapes fosters their visual understanding of the concept of volume.

Visual memory can be applied in physical education. When students are learning an exercise routine accompanied by music, the teacher can model in turn each portion of the routine initially without music, after which students visualize what they saw. The students then perform each part of the routine. Later the teacher can add music to the individual portions.

For an elementary language arts unit involving writing a paragraph that gives directions for performing a task or making something, a teacher might ask his or her students to think about and picture the individual steps (e.g., of making a peanut butter and jelly sandwich). Once students finish, they can visualize each step while writing it down.

Art teachers can use visual imagery to teach students to follow directions. The teacher might give the following directions orally and write them on the board: “Visualize on a piece of art paper a design including four circles, three triangles, and two squares, with some of the shapes overlapping one another.” The teacher might ask the following questions to ensure that students are using imagery: “How many circles do you see?” “How many triangles?” “How many squares?” “Are any of the shapes touching? Which ones?”

A dance teacher might have students listen to the music to which they will be performing. Then the students could imagine themselves dancing, visualizing every step and movement. The teacher also might ask students to visualize where they and their classmates are on the stage as they dance.

An American history teacher took his classes to a Civil War battlefield and had them imagine what it must have been like to fight a battle at that site. Later in class he had students construct with technology a map that duplicated the site and then create various scenarios for what could have happened as the Union and Confederate forces fought.

Researchers increasingly are studying the role of visualizations in learning. A  visualization  is a nonverbal symbolic or pictorial illustration such as a graph, realistic diagram, or picture (Höffler,  2010 ). A  dynamic visualization  is one that portrays change, such as a video and animation. Höffler reported that learners with low spatial ability seem better supported by dynamic rather than nondynamic visualizations. Further, segmenting a dynamic visualization (showing in pieces with interspersed pauses) may help to reduce extraneous cognitive load (see  Chapter 5 ), which can help students better process the representation (e.g., encode and store in LTM; Spanjers, van Gog, & van Merriënboer,  2010 ).

Evidence shows that people also can use visual imagery with abstract dimensions. Kerst and Howard ( 1977 ) asked students to compare pairs of cars, countries, and animals on the concrete dimension of size and on an appropriate abstract dimension (e.g., cost, military power, ferocity). The abstract and concrete dimensions yielded similar results: As items became more similar, reaction times increased. For instance, in comparing size, comparing a bobcat and an elephant is easier than comparing a rhinoceros and a hippopotamus. How participants imagined abstract dimensions or whether they even used imagery is not clear. Perhaps they represented abstract dimensions in terms of propositions, such as by comparing the United States and Jamaica on military power using the proposition, “(The) United States (has) more military power (than) Jamaica.” Knowledge maps, which are pictorial representations of linked ideas, aid student learning (O’Donnell, Dansereau, & Hall,  2002 ).

Visual Memory and LTM

Although researchers agree that visual memory is part of WM, they disagree about whether images are retained in LTM (Kosslyn & Pomerantz,  1977 ; Matlin,  2009 ; Pylyshyn,  1973 ).  Dual-code theory  directly addresses this issue (Clark & Paivio,  1991 ; Paivio,  1971 ,  1978 ,  1986 ). LTM has two means of representing knowledge: a verbal system incorporating knowledge expressed in language and an imaginal systemstoring visual and spatial information. These systems are interrelated—a verbal code can be converted into an imaginal code and vice versa—but important differences exist. The verbal system is suited for abstract information, whereas the imaginal system can be used to represent concrete objects or events.

Shepard’s experiments support the utility of imagery and offer indirect support for the dual-code theory. Other supporting evidence comes from research showing that when recalling lists of concrete and abstract words, people recall concrete words better than abstract ones (Terry,  2009 ). The dual-code theory explanation of this finding is that concrete words can be coded verbally and visually, whereas abstract words usually are coded only verbally. At recall, people draw on both memory systems for the concrete words, but only the verbal system for the abstract words. Other research on imaginal mnemonic mediators supports the dual-code theory ( Chapter 10 ).

In contrast,  unitary theory  postulates that all information is represented in LTM in verbal codes (propositions). Images in WM are reconstructed from verbal LTM codes. Indirect support for this notion comes from Mandler and Johnson ( 1976 ) and Mandler and Ritchey ( 1977 ). As with verbal material, people employ schemas while acquiring visual information. They remember scenes better when elements are in a typical pattern; memory is poorer when elements are disorganized. Meaningful organization and elaboration of information into schemas improve memory for scenes much as they do for verbal material. This finding suggests the operation of a common process regardless of the form of information presented.

This debate notwithstanding, using concrete materials and pictures enhances memory (Terry,  2009 ). Such instructional tools as manipulatives, audiovisual aids, and computer graphics facilitate learning. Although concrete devices are undoubtedly more important for young children because they lack the cognitive capability to think in abstract terms, students of all ages benefit from information presented in multiple modes.

Individual Differences

The extent to which people actually use visual memory varies as a function of cognitive development. Kosslyn ( 1980 ) proposed that children are more likely to use visual memory to remember and recall information than adults, who rely more on verbal representation. Kosslyn gave children and adults statements such as, “A cat has claws,” and “A rat has fur.” The task was to determine accuracy of the statements. Kosslyn reasoned that adults could respond quicker because they could access the propositional information from LTM, whereas children would have to recall the image of the animal and scan it. To control for adults’ better information processing in general, some adults were asked to scan an image of the animal, whereas others were free to use any strategy.

Adults were slower to respond when given the imagery instructions than when free to choose a strategy, but no difference was found for children. These results suggest that children use imagery even when they are free to do otherwise, but they do not address whether children cannot use verbal information (because of cognitive limitations) or whether they can but choose not to because they find imagery to be more effective.

Use of visual memory also depends on effectiveness of performing the component processes. Apparently two types are involved. One set of processes helps to activate stored memories of parts of images. Another set reconstructs the parts into the proper configuration. These processes may be localized in different parts of the brain. Individual differences in imagery can result because people differ in how effectively this dual processing occurs (Kosslyn,  1988 ).

The use of imagery by people of any age depends on what is to be imagined. Concrete objects are more easily imagined than abstractions. Another factor that influences use of imagery is one’s ability to employ it.  Eidetic imagery , or photographic memory (Leask, Haber, & Haber,  1969 ), actually is unlike a photograph; the latter is seen as a whole, whereas eidetic imagery occurs in pieces. People report that an image appears and disappears in segments rather than all at once.

Eidetic imagery is found more often in children than in adults (Gray & Gummerman,  1975 ), yet even among children it is uncommon (about 5%). Eidetic imagery may be lost with development, perhaps because verbal representation replaces visual thinking. It also is possible that adults retain the capacity to form clear images but do not routinely do so because their verbal systems can represent more information. The capacity to use visual memory can be improved, but most adults do not explicitly work to develop it.

TRANSFER

Transfer  refers to knowledge being applied in new ways, in new situations, or in familiar situations with different content. Transfer also explains how prior learning affects subsequent learning. Transfer is involved in new learning when students retrieve their prior relevant knowledge and experiences (National Research Council,  2000 ). The cognitive capability for transfer is important, because without it all learning would be situation specific, and much instructional time would be spent reteaching skills in different contexts.

There are different types of transfer.  Positive transfer  occurs when prior learning facilitates subsequent learning. Learning how to drive a car with standard transmission should facilitate learning to drive other cars with standard transmission.  Negative transfer  means that prior learning interferes with subsequent learning or makes it more difficult. Learning to drive a standard transmission car might have a negative effect on subsequently learning to drive a car with automatic transmission because one might try to hit the nonexistent clutch and possibly shift gears while the car is moving, which could ruin the transmission.  Zero transfer  means that one type of learning has no noticeable influence on subsequent learning. Learning to drive a standard transmission car should have no effect on learning to operate a computer.

Current cognitive conceptions of learning highlight the complexity of transfer (Phye,  2001 ; Taatgen,  2013 ). Although some forms of simple skill transfer seem to occur automatically, much transfer requires higher-order thinking skills and beliefs about the usefulness of knowledge. This section begins with a brief overview of historical perspectives on transfer, followed by a discussion of cognitive views and the relevance of transfer to school learning.

Historical Views

Identical Elements.

Behavior (conditioning) theories ( Chapter 3 ) stress that transfer depends on identical elements or similar features (stimuli) among situations. Thorndike ( 1913b ) contended that transfer occurs when situations have identical elements (stimuli) and call for similar responses. A clear and known relation must exist between the original and transfer tasks, as is often the case between drill/practice and homework.

This view is intuitively appealing. Students who learn to solve the problem 602 − 376 = ? are apt to transfer that knowledge and also solve the problem 503 − 287 = ? We might ask, however, what the elements are and how similar they must be to be considered identical. In subtraction, do the same types of numbers need to be in the same column? Teachers know that students who can solve the problem 42 − 37 = ? will not necessarily be able to solve the problem 7428 − 2371 = ?, even though the former problem is contained within the latter one. Findings such as this call into question the validity of identical elements. Furthermore, even when identical elements exist, students must recognize them. If students believe no commonality exists between situations, no transfer will occur. The identical elements position, therefore, is inadequate to explain all transfer.

Mental Discipline.

Also relevant to transfer is the  mental discipline  doctrine ( Chapter 3 ), which holds that learning certain subjects (e.g., mathematics, the classics) enhances general mental functioning and facilitates learning of new content better than does learning other subjects. This view was popular in Thorndike’s day and periodically reemerges in the form of recommendations for basic or core skills and knowledge (e.g., Hirsch,  1987 ).

Research by Thorndike ( 1924 ) provided no support for the mental discipline idea ( Chapter 3 ). Instead, Thorndike concluded that what facilitates new learning is students’ beginning level of mental ability. Students who were more intelligent when they began a course gained the most from the course. The intellectual value of studies reflects not how much they improve students’ ability to think but rather how they affect students’ interests and goals.

Generalization.

Skinner’s ( 1953 ) operant conditioning theory proposed that transfer involves  generalization  of responses from one discriminative stimulus to another. For example, students might be taught to put their books in their desks when the bell rings. When students go to another class, putting books away when the bell rings might generalize to the new setting.

The notion of generalization, like identical elements, has intuitive appeal. Surely some transfer occurs through generalization, and it may even occur automatically. Students who are punished for misbehavior in one class may not misbehave in other classes. Once drivers learn to stop their cars at a red light, then that response will generalize to other red lights regardless of location, weather, time of day, and so forth.

Nonetheless, the generalization position has problems. As with identical elements, we can ask what features of the situation are used to generalize responses. Situations share many common features, yet we respond only to some of them and disregard others. We respond to the red light regardless of many other features in the situation. At the same time, we might be more likely to run a red light when no other cars are around or when we are in a hurry. Our response is not fixed but rather depends on our cognitive assessment of the situation. The same can be said of other situations where generalization does not occur automatically. Cognitive processes are involved in most generalization as people determine whether responding in similar fashion is appropriate in that setting. The generalization position, therefore, is incomplete because it neglects the role of cognitive processes.

Activation of Knowledge in Memory

An information processing perspective contends that transfer involves activating knowledge in memory networks. It requires that information be cross-referenced with propositions linked in memory (Anderson,  1990 ). The more links between bits of information in memory, the likelier that activating one piece of information will cue other information in memory. Such links can be made within and between networks.

In other words, transfer depends on students recognizing the common “deep” structure between the learning and transfer contexts, especially when the  “surface” structures  of the situations may differ (Chi & VanLehn,  2012 ). Information in memory networks involving deep structure will facilitate transfer when learners recognize that structure in the transfer context.

The same process is involved in transfer of procedural knowledge and productions (Bruning et al.,  2011 ). Transfer occurs when knowledge and productions are linked in LTM with different content. Students must also believe that productions are useful in various situations. Transfer is aided by the uses of knowledge being stored with the knowledge itself. For example, learners may possess a production for skimming text. This may be linked in memory with other reading procedures (e.g., finding main ideas, sequencing) and may have various uses stored with it (e.g., skimming Web page text to get the gist, skimming memos to determine meeting place and time). The more links in LTM and the more uses stored with skimming, the better the transfer. Such links are formed by having students practice skills in various settings and by helping them understand the uses of knowledge. The general aspects of production rules (similar to “deep” structures) promote transfer (Taatgen,  2013 ). These general aspects are developed by combining task-specific features that learners accumulate over different experiences.

This cognitive description of transfer fits much of what we know about cued knowledge. Where more LTM links are available, accessing information in different ways is possible. We may not be able to recall the name of Aunt Martha’s dog by thinking about her (cuing the “Aunt Martha” network), but we might be able to recall the name by thinking about (cuing) breeds of dogs (“collie”). Such cuing is reminiscent of the experiences we periodically have of not being able to recall someone’s name until we think about that person from a different perspective or in a different context.

At the same time, we still do not know many things about how such links form. Links are not automatically made simply by pointing out uses of knowledge to students or having them practice skills in different contexts (National Research Council,  2000 ). The next section discusses different forms of transfer, which are governed by different conditions.

Types of Transfer

Table 6.2 Types of transfer.

Transfer is not a unitary phenomenon but rather is complex (Barnett & Ceci,  2002 ;  Table 6.2 ). One distinction is between near and far transfer (Royer,  1986 ). Near transfer occurs when situations overlap a great deal, such as between the stimulus elements during instruction and those present in the transfer situation. An example is when fraction skills are taught and then students are tested on the content in the same format in which it was taught. In contrast, far transfer involves a transfer context much different from that in which original learning occurred. An example would be applying fraction skills in an entirely different setting without explicitly being told to do so. Thus, students might have to add parts of a recipe (1/2 cup milk and 1/4 cup water) to determine the amount of liquid without being told the task involves fractions.

Another distinction is between literal and figural transfer. Literal transfer involves transfer of an intact skill or knowledge to a new task (Royer,  1986 ). Literal transfer occurs when students use fraction skills in and out of school. Figural transfer refers to using some aspect of our general knowledge to think or learn about a particular problem. Figural transfer often involves using analogies, metaphors, or comparable situations. Figural transfer occurs when students encounter new learning and employ the same study strategies that they used to master prior learning in a related area. Figural transfer requires drawing an analogy between the old and new situations and transferring that general knowledge to the new situation.

Although some overlap exists, the forms of transfer involve different types of knowledge. Near transfer and literal transfer involve primarily declarative knowledge and mastery of basic skills. Far transfer and figurative transfer involve declarative and procedural knowledge, as well as conditional knowledge concerning the types of situations in which the knowledge may prove useful (Royer,  1986 ).

Salomon and Perkins ( 1989 ) distinguished low-road from high-road transfer. Low-road transfer refers to transfer of well-established skills in a spontaneous and perhaps automatic fashion. In contrast, high-road transfer is abstract and mindful; it “involves the explicit conscious formulation of abstraction in one situation that allows making a connection to another” (Salomon & Perkins,  1989 , p. 118).

Low-road transfer occurs with skills and actions that have been practiced extensively in varied contexts. The behaviors tend to be performed automatically in response to characteristics of a situation that are similar to those of the situation in which they were acquired. Examples are learning to drive a car and then driving a different but similar car, brushing one’s teeth with a regular toothbrush and with an electric toothbrush, or solving algebra problems at school and at home. At times the transfer may occur with little conscious awareness of what one is doing. The level of cognitive activity increases when some aspect of the situation differs and requires attention. For example, most people have little trouble accommodating to features in rental cars. When features differ (e.g., the headlight control works differently or is in a different position from what one is used to), people have to learn them.

High-road transfer occurs when students learn a rule, principle, prototype, schema, and so forth, and then use it in a more general sense than how they learned it. Transfer is mindful because students do not apply the rule automatically. Rather, they examine the new situation and decide what strategies will be useful to apply. Abstraction is involved during learning and later when students perceive basic elements in the new problem or situation and decide to apply the skill, behavior, or strategy. Low-road transfer primarily involves declarative knowledge, and high-road transfer uses productions and conditional knowledge to a greater extent.

Salomon and Perkins ( 1989 ) distinguished two types of high-road transfer—forward reaching and backward reaching—according to where the transfer originates. Forward-reaching transfer occurs when one abstracts behavior and cognitions from the learning context to one or more potential transfer contexts. For example, while students are studying precalculus, they might think about how some of the material (e.g., limits) might be pertinent in calculus. Another example is while being taught in a class how a parachute works, students might think about how they will use the parachute in actually jumping from an airplane.

Forward-reaching transfer is proactive and requires self-monitoring of potential contexts and uses of skills and knowledge. To determine potential uses of precalculus, for example, learners must be familiar with other content knowledge of potential contexts in which knowledge might be useful. Forward-reaching transfer is unlikely when students have little knowledge about potential transfer contexts.

In backward-reaching transfer, students abstract in the transfer context features of the situation that allow for integration with previously learned ideas (Salomon & Perkins,  1989 ). While students are working on a calculus problem, they might try to think of any situations in precalculus that could be useful for solving the calculus problem. Students who have difficulty learning new material employ backward-reaching transfer when they think back to other times when they experienced difficulty and ask what they did in those situations (e.g., seek help from friends, conduct a Web search, reread the text, talk with the teacher). They then might be apt to implement one of those solutions in hopes of remedying their current difficulty. Analogical reasoning ( Chapter 7 ) might involve backward-reaching transfer, as students apply steps from the original problem to the current one. Consistent with the effects of analogical reasoning on learning, Gentner, Loewenstein, and Thompson ( 2003 ) found that  analogical reasoning enhanced transfer, especially when two original cases were presented together.

Earlier we noted that transfer involves linked information in LTM such that the activation of one item can cue other items. Presumably low-road transfer is characterized by relatively automatic cuing. A central distinction between the two forms is degree of mindful abstraction, or the volitional, metacognitively guided employment of nonautomatic processes (Salomon & Perkins,  1989 ). Mindful abstraction requires that learners not simply act based on the first possible response, but rather that they examine situational cues, define alternative strategies, gather information, and seek new connections between information. LTM cuing is not automatic with high-road transfer, but rather deliberate, and can result in links being formed in LTM as individuals think of new ways to relate knowledge and contexts.

Anderson, Reder, and Simon ( 1996 ) contended that transfer is more likely when learners attend to the cues that signal the appropriateness of using a particular skill. They then will be more apt to notice those cues on transfer tasks and employ the skill. In this sense, the learning and transfer tasks share symbolic elements. These shared elements are important in strategy transfer.

Strategy Transfer

Transfer applies to strategies as well as to skills and knowledge (Phye,  2001 ). An unfortunate finding of much research is that students learn strategies and apply them effectively but fail to maintain their use over time or generalize them beyond the instructional setting. This is a common issue encountered in problem solving ( Chapter 7 ; Jonassen & Hung,  2006 ). Many factors impede strategy transfer, including not understanding that the strategy is appropriate for different settings, not understanding how to modify its use with different content, believing that the strategy is not as useful for performance as other factors (e.g., time available), thinking that the strategy takes too much effort, or not having the opportunity to apply the strategy with new material (Borkowski & Cavanaugh,  1979 ; Dempster & Corkill,  1999 ; Paris, Lipson, & Wixson,  1983 ; Pressley et al.,  1990 ; Schunk & Rice,  1993 ).

Phye ( 1989 ,  1990 ,  1992 ,  2001 ; Phye & Sanders,  1992 ,  1994 ) developed a model useful for enhancing strategy transfer and conducted research testing its effectiveness. During the initial acquisition phase, learners receive instruction and practice to include assessment of their metacognitive awareness of the uses of the strategy. A later retention phase includes further practice on training materials and recall measures. The third transfer phase occurs when participants attempt to solve new problems that have different surface characteristics but that require the same solution strategy practiced during training. Phye also stressed the role of learner motivation for transfer and ways to enhance motivation by showing learners uses of knowledge. Motivation is a critical influence on transfer (National Research Council,  2000 ; Pugh & Bergin,  2006 ).

In one study in which adults worked on verbal analogy problems, some received corrective feedback during trials that consisted of identifying the correct solutions, whereas others were given advice concerning how to solve analogies. All students judged confidence in the correctness of solutions they generated. During training, corrective feedback was superior to advice in promoting transfer of problem-solving skills; however, on a delayed transfer task, no difference occurred between conditions. Regardless of condition, confidence in problem-solving capabilities bore a positive relation to actual performance. Butler, Godbole, and Marsh ( 2013 ) found that providing feedback that included an explanation of the correct answer produced better transfer than did feedback that only included the correct answer.

In addition to knowledge of the strategy, transfer requires knowledge of the uses of the strategy, which is facilitated when learners explain the strategy as they learn it (Crowley & Siegler,  1999 ). Feedback about how the strategy helps improve performance facilitates strategy retention and transfer (Phye & Sanders,  1994 ; Schunk & Swartz,  1993a ,  1993b ). Phye’s research highlights the link of strategy transfer with information processing and the key roles played by practice, corrective feedback, and motivation. It also underscores the point that teaching students self-regulated learning strategies can facilitate transfer (Fuchs et al.,  2003 ; Fuchs, Fuchs, Finelli, Courey, & Hamlett,  2004 ;  Chapter 10 ).  Application 6.6  has suggestions for ways to facilitate transfer.

APPLICATION 6.6 Facilitating Transfer

Ms. DiGiorgio helps her elementary students build on the knowledge they already have learned. She has her students recall the major points of each page of a story in their reading book before they write a summary paragraph about the story. She also reviews with them how to develop a complete paragraph. Building on former learning helps her children transfer knowledge and skills to a new activity.

In preparing for a class discussion about influential presidents of the United States, Mr. Neufeldt sends a study sheet home with his high school students asking them to list presidents that they feel had a major impact on American history. He instructs them not only to rely on what has been discussed in class, but also to rely on knowledge they have from previous courses or other readings and research they have done. He encourages students to pull the information together from the class discussion and incorporate the former learning into the learning that occurs from the new material.

INSTRUCTIONAL APPLICATIONS

As noted in  Chapter 5 , information processing principles increasingly have been applied to school learning settings. This section described retrieval applications: encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.

Encoding-Retrieval Similarity

We saw earlier that memory benefits from encoding specificity, or the idea that the learning conditions at retrieval match as closely as possible those present during encoding. The term “encoding specificity” omits “retrieval,” which can convey the erroneous impression that encoding is the most important process and that once encoding occurs, retrieval will happen. Suprenant and Neath ( 2009 ) underscore the importance of retrieval and present an encoding-retrieval principle of memory, which states that memory depends heavily on the relation between the conditions at encoding and those at retrieval. This relation is referred to here as encoding-retrieval similarity.

An instructional implication of encoding-retrieval similarity is to have the same or similar context at retrieval that was present at encoding. For example, students who learn in a computer-based learning environment (e.g., online) might be tested in the same environment. Students who learn to solve algebra problems written in particular formats might be tested with similar problems. The prediction is that the similarity between encoding and retrieval conditions should facilitate memory and performance.

But as we have seen in this chapter, transfer is important. Educators want students to be able to transfer their skills beyond the conditions present at encoding and retrieve them under different conditions. Teachers can facilitate transfer by helping students encode a reminder that they can subsequently retrieve and that will promote further retrieval. For example, if students are learning a strategy for comprehending written text, the teacher might label this strategy “the steps,” then tell students that when they have to answer comprehension questions to think of “the steps.” Such a reminder should cue retrieval of the strategy’s steps for comprehension.

The educators in the opening vignette lament the need for many reviews because students seem to forget so much, even over long weekends. It is possible that students have not forgotten the content but rather cannot retrieve it due to inadequate cues. Providing more cues at retrieval may help lessen the need for reviews. Under what conditions did students learn the material? Did they work individually or in groups? Whole class or small groups? Computer-based learning environment? What content was associated with the original learning? When students return from a long break, teachers can cue not only the content learned but also the conditions under which students learned it. For example, a teacher might remind students that they studied this content last week Thursday afternoon, when they worked in small groups on computers studying environmental pollution.

Retrieval-Based Learning

Retrieval is often thought of as an end product of learning (encoding); that is, retrieval happens after learning occurs. In fact, retrieval can serve a learning function. Karpicke and Grimaldi ( 2012 ) postulate that retrieval can affect learning directly and indirectly. Retrieval affects learning directly because when we retrieve knowledge we alter it and enhance our capability to reconstruct that knowledge in the future. Indirect retrieval effects on learning occur when retrieval affects other variables that in turn can influence learning. For example, when instructors ask students questions in class, students attempt to retrieve knowledge, and the success of their retrieval gives them feedback about how well they know the material. Such feedback may motivate them to study harder and may affect their sense of self-efficacy for performing well in the class.

There are many ways that teachers use retrieval to promote learning including class questions and discussions, tests, and quizzes. Yet the opening vignette shows that teachers do not like to engage in so many review sessions, and few would advocate for more testing. Quizzes always can be given for students to check their levels of understanding (no grades), perhaps at the end of learning sessions. But there are other ways to effectively use retrieval as a learning process.

One means is to have students use retrieval when they study. Students may believe that studying involves mostly re-reading, but studying also can include frequent times when students stop reading and attempt to recall what they have read. The recall is a form of active rehearsal. Studying plus retrieval produces superior learning compared with studying alone (Karpicke & Grimaldi,  2012 ).

Another suggestion is to have students construct concept maps that link in networks related concepts in memory. Students can do this as they work in class or study on their own. Teachers can facilitate this process by asking students to construct maps that reflect not only concepts directly related to one another but also concepts requiring inferences (e.g., the example used earlier about when the vice president would vote in the Senate).

Students may not be aware of the potential benefits of retrieval on learning, which suggests that teaching students retrieval strategies (e.g., self-cuing) may be helpful. Retrieval is a key process of academic studying stressed by self-regulated learning researchers ( Chapter 10 ). An abundance of research shows that students can be taught self-regulated learning strategies and can transfer them outside of the learning context to improve their academic performances (Zimmerman & Schunk,  2011 ).

Some other effective ways to build retrieval into learning settings include reciprocal teaching ( Chapter 8 ) and computer-based  learning methods  ( Chapter 7 ). Computer-based systems can be programmed to guide students’ retrieval (Karpicke & Grimaldi,  2012 ). For example, the system has students engage in repeated retrieval but the study decisions are made by the system, not the student. This type of arrangement takes into account individual student differences, as some students will benefit from more retrieval opportunities than others will.

Retrieval-based learning can have motivational effects ( Chapter 9 ). Students who can retrieve knowledge are apt to experience heightened self-efficacy for performing well ( Chapter 4 ; Schunk & Pajares,  2009 ). The belief that they have learned may motivate them to continue to apply themselves to further develop their learning. Thus, the indirect motivational effects of retrieval on learning may continue to strengthen self-efficacy and lead to further retrieval and learning.

Teaching for Transfer

Although there are different forms of transfer, they often work in concert. While students complete a task, some behaviors may transfer automatically whereas others may require mindful application. For example, assume that Jeff is writing a short paper. In thinking through the organization, Jeff might employ high-road, backward-reaching transfer by thinking about how he organized papers in previous, similar situations. Many aspects of the task, including word choice and spellings, will occur automatically (low-road transfer). As Jeff writes, he also might think about how this information could prove useful in other settings. Thus, if the paper is on some aspect of the Civil War, Jeff might think of how to use this knowledge in history class. Salomon and Perkins ( 1989 ) cited an example involving chess masters, who accumulate a repertoire of configurations from years of play. Although some of these may be executed automatically, expert play depends on mindfully analyzing play and potential moves. It is strategic and involves high-road transfer.

In some situations, low-road transfer could involve a good degree of mindfulness. With regard to strategy transfer, even minor variations in formats, contexts, or requirements can make transfer problematic among students, especially among those who experience learning problems (Borkowski & Cavanaugh,  1979 ). Conversely, some uses of analogical reasoning can occur with little conscious effort if the analogy is relatively clear. A good rule is never to take transfer for granted; it must be directly addressed.

This raises the issue of how teachers might encourage transfer in students. A major goal of teaching is to promote long-term retention and transfer (Halpern & Hakel,  2003 ). We know that having students practice skills in varied contexts and ensuring that they understand different uses for knowledge builds links in LTM (Anderson, Reder, & Simon,  1996 ). Homework is a mechanism for transfer because students practice and refine, at home, skills learned in school. Research shows a positive relation between homework and student achievement with the relation being stronger in grades 7–12 than in grades K–6 (Cooper, Robinson, & Patall,  2006 ).

But students do not automatically transfer strategies for the reasons noted earlier. Practice addresses some of these concerns, but not others. Cox ( 1997 ) recommended that as students learn in many contexts, they should determine what they have in common. Complex skills, such as comprehension and problem solving, will probably benefit most from this situated cognition approach (Griffin,  1995 ). Motivation should be addressed (Pugh & Bergin,  2006 ). Teachers may need to provide students with explicit motivational feedback that links strategy use with improved performance and provides information about how the strategy will prove useful in that setting. Studies show that such motivational feedback enhances strategy use, academic performance, and self-efficacy for performing well (Schunk & Rice,  1993 ).

Students also should establish academic goals (a motivational variable), the attainment of which requires careful deliberation and use of available resources. By cuing students at appropriate times, teachers may help them use relevant knowledge in new ways. Teachers might ask a question such as, “What do you know that might help you in this situation?” Such cuing tends to be associated with greater generation of ideas. Teachers can serve as models for transfer. Modeling strategies that bring related knowledge to bear on a new situation encourage students to seek ways to enhance transfer in both forward-and backward-reaching fashion and feel more efficacious about doing so. Working with children in grades 3–5 during mathematical problem solving, Rittle-Johnson ( 2006 ) found that having children explain how answers were arrived at and whether they were correct promoted transfer of problem-solving strategies.

SUMMARY

Retrieval is a key component of information processing. Retrieval is the successful result of encoding but also can facilitate learning. When learners have to retrieve knowledge, the appropriate cues enter WM and activate LTM networks through spreading activation. For verbal knowledge, the learner’s WM constructs a response when the knowledge is obtained. Memory search continues until knowledge is retrieved. An unsuccessful search yields no information. Much retrieval occurs automatically.

Certain conditions affect the efficacy of retrieval. One is encoding specificity, which means that retrieval proceeds best when retrieval cues and conditions match those present at encoding. Other conditions that facilitate retrieval are elaboration, meaningfulness, and organization of knowledge in LTM. Presumably these conditions promote spreading activation and access by learners of needed memory networks.

An area that illustrates the storage and retrieval of information in LTM is language comprehension, which involves perception, parsing, and utilization. Communications are incomplete; speakers omit information they expect that listeners will know. Effective language comprehension requires that listeners possess adequate propositional knowledge and schemas and understand the context. To integrate information into memory, listeners identify given information, access it in LTM, and relate new information to it. Language comprehension is a central aspect of literacy and relates strongly to academic success—especially in subjects that require extensive reading.

Table 6.3 Summary of learning issues.

Even when knowledge is encoded, it may be forgotten. Forgetting refers to the loss of information from memory or the failure to access it. Failure to retrieve may result from decay of information or interference. Factors that facilitate retrieval and lessen the chance of forgetting are the strength of the original encoding, the number of alternative memory networks, and the amount of distortion or merging of information. Retrieval always involves some amount of re-construction of knowledge as learners access information in LTM.

Because forgetting occurs, relearning often is necessary. Research evidence shows that relearning typically is easier than new learning, which suggests that some amount of knowledge in LTM is not forgotten but rather difficult to access. The savings score indicates the amount of time or number of trials necessary for relearning as a percentage of the amount of time or number of trials necessary for original learning. A testing effect occurs when taking tests or quizzes enhances learning and retention such that scores on the final test are higher than if prior testing had not occurred. Although this is not an argument for more testing of students, research supports the point that testing seems to facilitate retention and relearning and perhaps better than additional studying. To lessen evaluative pressures, teachers can give students nongraded quizzes at the end of learning sessions.

Much evidence exists for information being stored in memory in verbal form (meanings), but there also is evidence for visual memory. Visual/spatial knowledge is stored as an analog representation: It is similar but not identical to its referents. Dual-code theory postulates that the imaginal system primarily stores concrete objects and events and the verbal system stores more abstract information expressed in language. Conversely, images may be reconstructed in WM from verbal codes stored in LTM. Developmental evidence shows that children are more likely than adults to represent knowledge as images, but visual memory can be developed in persons of any age.

Transfer is a complex phenomenon. Historical views include identical elements, mental discipline, and generalization. From a cognitive perspective, transfer involves activation of memory networks and occurs when information is linked. Distinctions are drawn between near and far, literal and figural, and low-road and high-road transfer. Some forms of transfer may occur automatically, but much is conscious and involves abstraction and recognizing underlying structures. Providing students with feedback on the usefulness of skills and strategies makes transfer more likely to occur.

The importance of retrieval and transfer for learning suggests some educational applications. Three that are pertinent involve encoding-retrieval specificity, retrieval-based learning, and teaching for transfer.

Chapter 8 Constructivism

Ms. Rahn, a sixth-grade middle school science teacher, is sitting at a table with four students. They are about to perform an experiment on the physical properties of matter called the “mystery substance experiment.” On the table are the following materials: mixing bowl, 16 ounces of cornstarch, measuring cup, bottles of water, spoon, scissors, plate, and paper towels.

Constructivism  is a psychological and philosophical perspective contending that individuals form or construct much of what they learn and understand (O’Donnell,  2012 ). A major influence on constructivism is theory and research in human development, especially the theories of Piaget and Vygotsky (discussed in this chapter). The emphasis that these theories place on the role of knowledge construction is central to constructivism.

Over the past several years, constructivism increasingly has been applied to learning and teaching. The history of learning theory reveals a shift away from environmental influences and toward human factors as explanations for learning. Cognitive theorists and researchers ( Chapters 4 – 7 ) disputed the claim of behaviorism ( Chapter 3 ) that stimuli, responses, and consequences were adequate to explain learning. Cognitive theories place great emphasis on learners’ information processing as a central cause of learning. Despite the elegance of cognitive learning theories, some researchers believe that these theories fail to capture the complexity of human learning. This point is underscored by the fact that some cognitive perspectives use behavioral terminology such as the “automaticity” of performance and “forming connections” between items in memory.

Many contemporary learning researchers have shifted toward a stronger focus on learners. Rather than talk about how knowledge is acquired, they speak of how it is constructed. Although these researchers differ in their emphasis on factors that affect learning and learners’ cognitive processes, the theoretical perspectives they espouse may be loosely grouped and referred to as  constructivism . Learners’ constructions of understandings are evident in the opening vignette.

This chapter begins by providing an overview of constructivism including a description of its key assumptions and the different types of constructivist theories. The theories of Piaget, Bruner, and Vygotsky are described next, with emphasis on those aspects relevant to learning. The critical roles of private speech and socially mediated learning are explained. The chapter concludes with a discussion of constructivist learning environments and instructional applications that reflect principles of constructivism.

· When you finish studying this chapter, you should be able to do the following:

· ■ Discuss the major assumptions and various types of constructivism.

· ■ Summarize the major processes in Piaget’s theory that are involved in learning and some implications for instruction.

· ■ Discuss the types of knowledge representation proposed by Bruner and what is meant by the “spiral curriculum.”

· ■ Explain the key principles of Vygotsky’s sociocultural theory and implications for teaching in the zone of proximal development.

· ■ Explain how private speech can affect learning and the benefits of socially mediated learning.

· ■ List the key features of constructivist learning environments and the major components of the APA learner-centered principles.

· ■ Explain how teachers can become more reflective and thereby enhance student achievement.

· ■ Describe how discovery learning, inquiry teaching, and discussions and debates can be structured to reflect constructivist principles.

ASSUMPTIONS AND PERSPECTIVES

Many researchers and practitioners question some of classic information processing theory’s assumptions about learning and instruction because they believe that these assumptions do not completely explain students’ learning and understanding. These questionable assumptions of the classic view are as follows (Greeno,  1989 ):

· ■ Thinking resides in the mind rather than in interaction with persons and situations.

· ■ Processes of learning and thinking are relatively uniform across persons, and some situations foster higher-order thinking better than others.

· ■ Thinking derives from knowledge and skills developed in formal instructional settings more than on general conceptual competencies that result from one’s experiences and innate abilities.

Constructivists do not accept these assumptions because of evidence that thinking takes place in situations and that cognitions are largely constructed by individuals as a function of their experiences in these situations (Bredo,  1997 ). Constructivist accounts of learning and development highlight the contributions of individuals to what is learned.  Social constructivist  models further emphasize the importance of social interactions in acquisition of skills and knowledge. Let us examine further what constructivism is, its assumptions, and its forms.

Overview

What Is Constructivism?

There is a lack of consensus about the meaning of constructivism (Harlow, Cummings, & Aberasturi,  2006 ). Strictly speaking, constructivism is not a theory but rather an  epistemology , or philosophical explanation about the nature of learning (Hyslop-Margison & Strobel,  2008 ; Simpson,  2002 ). A theory is a scientifically valid explanation for learning ( Chapter 1 ). Theories allow for hypotheses to be generated and tested. Constructivism does not propound that learning principles exist and are to be discovered and tested, but rather that learners create their own learning. Readers who are interested in exploring the historical and philosophical roots of constructivism are referred to Bredo ( 1997 ) and Packer and Goicoechea ( 2000 ).

Nonetheless, constructivism makes general predictions that can be tested. Although these predictions are general and open to different interpretations (i.e., what does it mean that learners construct their own learning?), they can be the focus of research.

Constructivist theorists reject the notion that scientific truths exist and await discovery and verification. They argue that no statement can be assumed as true but rather should be viewed with reasonable doubt. The world can be mentally constructed in many different ways, so no theory has a lock on the truth. This is true even for constructivism: There are many varieties, and no one version should be assumed to be more correct than any other (Simpson,  2002 ).

Rather than viewing knowledge as truth, constructivists construe it as a working hypothesis. Knowledge is not imposed from outside people but rather formed inside them. A person’s constructions are true to that person but not necessarily to anyone else. This is because people produce knowledge based on their beliefs and experiences in situations (Cobb & Bowers,  1999 ), which differ from person to person. All knowledge, then, is subjective and personal and a product of our cognitions (Simpson,  2002 ). Learning is situated in contexts (Bredo,  2006 ).

Assumptions.

Constructivism highlights the interaction of persons and situations in the acquisition and refinement of skills and knowledge (Cobb & Bowers,  1999 ). Constructivism contrasts with conditioning theories that stress the influence of the environment on the person as well as with information processing theories that place the locus of learning within the mind with less attention to the context in which it occurs. Constructivism shares with social cognitive theory the assumption that persons, behaviors, and environments interact in reciprocal fashion (Bandura,  1986 ,  1997 ).

A key assumption of constructivism is that people are active learners and develop knowledge for themselves (Simpson,  2002 ). To understand material well, learners must discover the basic principles, as the students in the opening vignette were striving to do. Constructivists differ in the extent to which they ascribe this function entirely to learners. Some believe that mental structures come to reflect reality, whereas others (radical constructivists) believe that the individual’s mental world is the only reality. Constructivists also differ in how much they ascribe the construction of knowledge to social interactions with teachers, peers, parents, and others (Bredo,  1997 ).

Many of the principles, concepts, and ideas discussed in this text reflect the idea of constructivism, including cognitive processing, expectations, values, and perceptions of self and others. Thus, although constructivism seems to be a recent arrival on the learning scene, its basic premise that learners construct understandings underlies many learning principles. This is the epistemological aspect of constructivism. Some constructivist ideas are not as well developed as those of other theories discussed in this text, but constructivism has affected theory and research in learning and development.

Constructivism also has influenced educational thinking about curriculum and instruction. It underlies the emphasis on the integrated curriculum in which students study a topic from multiple perspectives. For example, in studying hot-air balloons, students might read about them, write about them, learn new vocabulary words, visit one (hands-on experience), study the scientific principles involved, draw pictures of them, and learn songs about them. Constructivist ideas also are found in many professional standards and affect the design of curriculum and instruction, such as the learner-centered principles developed by the American Psychological Association (discussed later).

Another constructivist assumption is that teachers should not teach in the traditional sense of delivering instruction to a group of students. Rather, they should structure situations such that learners become actively involved with content through manipulation of materials and social interaction. How the teacher in the opening vignette structured the lesson allowed students to construct their understandings of what was happening. Activities include observing phenomena, collecting data, generating and testing hypotheses, and working collaboratively with others. Classes visit sites outside of the classroom. Teachers from different disciplines plan the curriculum together. Students are taught to be self-regulated learners by setting goals, monitoring and evaluating progress, and going beyond basic requirements by exploring interests (Bruning, Schraw, & Norby,  2011 ).

Perspectives

Table 8.1 Perspectives on constructivism.

Constructivism is not a single viewpoint but rather has different perspectives ( Table 8.1 ; Bruning et al.,  2011 ; Phillips,  1995 ).  Exogenous constructivism  refers to the idea that the acquisition of knowledge represents a reconstruction of structures that exist in the external world. This view posits a strong influence of the external world on knowledge construction, such as by experiences, teaching, and exposure to models. Knowledge is accurate to the extent it reflects that reality. Contemporary information processing theories reflect this notion (e.g., schemas, productions, memory networks;  Chapter 5 ).

In contrast,  endogenous constructivism  emphasizes the coordination of cognitive actions (Bruning et al.,  2011 ). Mental structures are created from earlier structures, not directly from environmental information; therefore, knowledge is not a mirror of the external world acquired through experiences, teaching, or social interactions. Knowledge develops through the cognitive activity of abstraction and follows a generally predictable sequence. Piaget’s ( 1970 ) theory of cognitive development (discussed later) fits this framework.

Between these extremes lies  dialectical constructivism  (or  cognitive constructivism ), which holds that knowledge derives from interactions between persons and their environments. Constructions are not invariably bound to the external world nor are they wholly the result of the workings of the mind; rather, they reflect the outcomes of mental contradictions that result from interactions with the environment. This perspective has become closely aligned with many contemporary theories. For example, it is compatible with Bandura’s ( 1986 ) social cognitive theory ( Chapter 4 ) and with many motivation theories ( Chapter 9 ). The developmental theories of Bruner and Vygotsky (discussed later) also emphasize the influence of the social environment.

Each of these perspectives has merit and is potentially useful for research and teaching. Exogenous views are appropriate when we are interested in determining how accurately learners perceive the structure of knowledge within a domain. The endogenous perspective is relevant to explore how learners develop from novices through greater levels of competence ( Chapter 7 ). The dialectical view is useful for designing interventions to challenge children’s thinking and for research aimed at exploring the effectiveness of social influences such as exposure to models and peer collaboration.

Situated Cognition

A core premise of constructivism is that cognitive processes (including thinking and learning) are situated (located) in physical and social contexts (Anderson, Reder, & Simon,  1996 ; Cobb & Bowers,  1999 ; Greeno & the Middle School Mathematics Through Applications Project Group,  1998 ).  Situated cognition  (or  situated learning ) involves relations between a person and a situation; cognitive processes do not reside solely in one’s mind (Greeno,  1989 ).

The idea of person–situation interaction is not new. Most contemporary theories of learning and development assume that beliefs and knowledge are formed as people interact in situations. This emphasis contrasts with the classical information processing model that highlights the processing and movement of information through mental structures (e.g., sensory registers, working memory [WM], long-term memory [LTM];  Chapter 5 ). Information processing downplays the importance of situations once environmental inputs are received. Research in a variety of disciplines—including cognitive psychology, social cognitive learning, and content domains (e.g., reading, mathematics)—shows this to be a limited view and that thinking involves an extended reciprocal relation with the context (Bandura,  1986 ; Cobb & Bowers,  1999 ; Greeno,  1989 ).

Research highlights the importance of exploring situated cognition as a means of understanding the development of competence in domains such as literacy, mathematics (as we see in the opening scenario), and science (Cobb,  1994 ; Cobb & Bowers,  1999 ; Driver, Asoko, Leach, Mortimer, & Scott,  1994 ;  Chapter 7 ). Situated cognition also is relevant to motivation ( Chapter 9 ). As with learning, motivation is not an entirely internal state as posited by classical views or wholly dependent on the environment as predicted by  reinforcement theories  ( Chapter 3 ). Rather, motivation depends on cognitive activity in interaction with sociocultural and instructional factors, which include language and forms of assistance such as scaffolding (Sivan,  1986 ).

Situated cognition addresses the intuitive notion that many processes interact to produce learning. We know that motivation and instruction are linked: Good instruction can raise motivation for learning, and motivated learners seek effective instructional environments (Schunk & Pajares,  2009 ). A situated cognition perspective also leads researchers to explore cognition in authentic learning contexts such as schools, workplaces, and homes, many of which involve mentoring or apprenticeships.

Researchers have found that situated learning is effective. Griffin ( 1995 ), for example, compared traditional (in-class) instruction on map skills with a situated learning approach in which college students received practice in the actual environments depicted on the maps. The situated learning group performed better on a map-skill assessment. Although Griffin found no benefit of situated learning on transfer, the results of situated learning studies should be highly generalizable to similar contexts.

Situated cognition also is relevant to beliefs about how learning occurs (Greeno & the Middle School Mathematics Through Applications Project Group,  1998 ). Students exposed to a certain procedure for learning a subject experience situated cognition for that method; in other words, that is how this content is learned. For example, if students repeatedly receive mathematics instruction taught in didactic fashion by a teacher explaining and demonstrating, followed by their engaging in independent problem solving at their desks, then mathematics learning is apt to become situated in this context. The same students might have difficulty adjusting to a new teacher who favors using guided discovery (as done by the teacher in the opening lesson) by collaborative peer groups.

The instructional implication is that teaching methods should reflect the outcomes we desire in our learners. If we are trying to teach them inquiry skills, the instruction must incorporate inquiry activities. The method and the content must be properly situated.

Situated cognition fits well with the constructivist idea that context is an inherent part of learning. Nonetheless, extending the idea of situated learning too far may be erroneous. As Anderson, Reder, and Simon ( 1996 ) showed, there is plenty of empirical evidence for contextual independence of learning and transfer of learning between contexts. We need more information on which types of learning proceed best when they are firmly linked to contexts and when it is better to teach broader skills and show how they can be applied in different contexts.

Contributions and Implications

It is difficult to determine the contributions of constructivism because it is not a unified approach that offers specific hypotheses to be tested. Bereiter ( 1994 ) noted that the claim that “students construct their own knowledge” is not falsifiable but rather is true of all cognitive learning theories. Cognitive theories view the mind as a repository of beliefs, values, expectations, schemata, and so forth, so any feasible explanation of how those thoughts and feelings come to reside in the mind must assume that they are formed there. For example, social cognitive theory emphasizes the roles of expectations (e.g., self-efficacy, outcome) and goals; these beliefs and cognitions do not arise from nowhere but, rather, are constructed by learners.

Constructivism eventually must be evaluated not on whether its premises are true or false. Rather, it seems imperative to determine the process whereby students construct knowledge and how social, developmental, and instructional factors may influence that process. Research also is needed on when situational influences have greater effects on mental processes. A drawback of many forms of constructivism is the emphasis on  relativism  (Phillips,  1995 ), or the idea that all forms of knowledge are justifiable because they are constructed by learners, especially if they reflect societal consensus. Educators cannot accept this premise in good conscience because education demands that we inculcate certain values (e.g., honesty, fairness, responsibility) in our students regardless of whether some societal constituencies do not deem them important.

Furthermore, nature may constrain our thinking more than we wish to admit. Research suggests that some mathematical competencies—such as one-to-one correspondence and being able to count—are not constructed but rather largely genetically driven (Geary,  1995 ). Far from being relative, some forms of knowledge may be universally endogenous. Acquisition of other competencies (e.g., multiplying, word processing) requires environmental input. Constructivism—with its emphasis on minimal instructional guidance—may downplay the importance of human cognitive structures. Instructional methods that map better onto this cognitive structure may actually produce better learning (Kirschner, Sweller, & Clark,  2006 ). Researchers will determine the scope of constructivist processes in the sequence of competency acquisition and how these processes change as a function of development (Muller, Sokol, & Overton,  1998 ).

Constructivism has important implications for instruction and curriculum design (Phillips,  1995 ). The most straightforward recommendations are to involve students actively in their learning and to provide experiences that challenge their thinking and force them to rearrange their beliefs. Constructivism also underlies the current emphasis on  reflective teaching  (discussed later in this chapter). Social constructivist views (e.g., Vygotsky’s) stress that social group learning and peer collaboration are useful (Ratner, Foley, & Gimpert,  2002 ). As students model for and observe each other, they not only teach skills but also experience higher self-efficacy for learning (Schunk,  1995 ).  Application 8.1  gives constructivist applications. We now turn to a more in-depth examination of constructivism and its applications to human learning.

PIAGET’S THEORY OF COGNITIVE DEVELOPMENT

Piaget’s theory of cognitive development reflects the fundamental ideas of constructivism. Piaget’s theory is complex; a complete summary is beyond the scope of this text. Interested readers should consult other sources (Brainerd,  2003 ; Furth,  1970 ; Ginsburg & Opper,  1988 ; Phillips,  1969 ; Piaget,  1952 ,  1970 ; Piaget & Inhelder,  1969 ; Wadsworth,  1996 ). This section presents a concise overview of the major points relevant to constructivism and learning. Although Piaget’s theory currently is not a leading theory of cognitive development, it remains important and has several useful implications for instruction and learning.

APPLICATION 8.1 Constructivism and Teaching

Constructivism emphasizes integrated curricula and having teachers use materials in such a way that learners become actively involved. Mr. Rotaub implements various constructivist ideas in his fourth-grade classroom using integrated units. In the fall he presents a unit on pumpkins. In social studies children learn where pumpkins are grown and about the products made from pumpkins. They also study the uses of pumpkins in history and the benefits of pumpkins to early settlers.

He takes his class on a field trip to a pumpkin farm, where they learn how pumpkins are grown. Each student selects a pumpkin and brings it back to class. The pumpkin becomes a valuable learning tool. In mathematics the students estimate the size and weight of their pumpkins and then measure and weigh them. They establish class graphs by comparing all the pumpkins by size, weight, shape, and color. The children also estimate the number of seeds they think one pumpkin has, and then they count the seeds when he cuts it open. For art they design a shape for the carving of a pumpkin, and then he carves it. In language arts they write a story about pumpkins. They also write a thank-you letter to the pumpkin farm. For spelling, he uses words that they have used in the study of pumpkins. These examples illustrate how the study of pumpkins is integrated across the curriculum.

Developmental Processes

Equilibration.

According to Piaget, cognitive development depends on four factors: biological maturation, experience with the physical environment, experience with the social environment, and equilibration. The first three are self-explanatory, but their effects depend on the fourth.  Equilibration  refers to a biological drive to produce an optimal state of equilibrium (or  adaptation ) between cognitive structures and the environment (Duncan,  1995 ). Equilibration is the central factor and the motivating force behind cognitive development. It coordinates the actions of the other three factors and makes internal mental structures and external environmental reality consistent with each other.

To illustrate the role of equilibration, consider 6-year-old Allison riding in a car with her father. They are going 65 mph, and about 100 yards in front of them is a car. They have been following this car for some time, and the distance between them stays the same. Her dad points to the car and asks Allison, “Which car is going faster, our car or that car, or are we going the same speed?” Allison replies that the other car is going faster. When her dad asks why, she replies, “Because it’s in front of us.” If her dad then said, “We’re actually going the same speed,” this would create a conflict for Allison. She believes the other car is going faster, but she has received conflicting environmental input.

To resolve this conflict, Allison can use one of the two component processes of equilibration: assimilation and accommodation.  Assimilation  refers to fitting external reality to the existing cognitive structure. When we interpret, construe, and frame, we alter the nature of reality to make it fit our cognitive structure. To assimilate the information, Allison might alter reality by believing that her dad is teasing her or that perhaps at that moment the two cars were going the same speed but that the other car had been going faster beforehand.

Accommodation  refers to changing internal structures to provide consistency with external reality. We accommodate when we adjust our ideas to make sense of reality. To accommodate her belief system (structures) to the new information, she might believe her dad without understanding why or she might change her belief system to include the idea that all cars in front of them are going the same speed as they are.

Assimilation and accommodation are complementary processes. As reality is assimilated, structures are accommodated.

Stages.

· Piaget concluded from his research that children’s cognitive development passed through a fixed sequence. The pattern of operations that children can perform may be thought of as a level or stage. Each level or stage is defined by how children view the world. Piaget’s and other stage theories make certain assumptions:

· ■ Stages are discrete, qualitatively different, and separate. Progression from one stage to another is not a matter of gradual blending or continuous merging.

· ■ The development of cognitive structures is dependent on preceding development.

· ■ Although the order of structure development is invariant, the age at which one may be in a particular stage will vary from person to person. Stages should not be equated with ages.

Table 8.2  shows how Piaget characterized his stage progression. Much has been written on these stages, and an extensive research literature exists on each. The stages are only briefly described here; interested readers should consult other sources (Brainerd,  2003 ; Meece,  2002 ; Wadsworth,  1996 ).

Table 8.2 Piaget’s stages of cognitive development.

In the  sensorimotor  stage, children’s actions are spontaneous and represent an attempt to understand the world. Understanding is rooted in present action; for example, a ball is for throwing and a bottle for sucking. The period is characterized by rapid change; a 2-year-old is cognitively far different from an infant. Children actively equilibrate, albeit at a primitive level. Cognitive structures are constructed and altered, and the motivation to do this is internal. The notion of  effectance motivation  ( mastery motivation ;  Chapter 9 ) is relevant to sensorimotor children. By the end of the sensorimotor period, children have attained sufficient cognitive development to progress to new conceptual-symbolic thinking characteristic of the preoperational stage (Wadsworth,  1996 ).

Preoperational  children are able to imagine the future and reflect on the past, although they remain heavily perceptually oriented in the present. They are apt to believe that 10 coins spread out in a row are more than 10 coins in a pile. They also are unable to think in more than one dimension at a time; thus, if they focus on length, they are apt to think a longer object (a yardstick) is bigger than a shorter one (a brick) even though the shorter one is wider and deeper. Preoperational children demonstrate  irreversibility ; that is, once things are done, they cannot be changed (e.g., the box flattened cannot be remade into a box). They have difficulty distinguishing fantasy from reality. Cartoon characters appear as real as people. The period is one of rapid language development. Another characteristic is that children become less  egocentric : They realize that others may think and feel differently than they do.

The  concrete operational  stage is characterized by remarkable cognitive growth and is a formative one in schooling, because it is when children’s language and basic skills acquisition accelerate dramatically. Children begin to show some abstract thinking, although it typically is defined by properties or actions (e.g., honesty is returning money to the person who lost it). Concrete operational children display less egocentric thought, and language increasingly becomes social.  Reversibility  in thinking is acquired along with classification and seriation—concepts essential for the acquisition of mathematical skills. Concrete operational thinking no longer is dominated by perception; children draw on their experiences and are not always swayed by what they perceive.

The  formal operational  stage extends concrete operational thought. No longer is thought focused exclusively on tangibles; children are able to think about hypothetical situations. Reasoning capabilities improve, and children can think about multiple dimensions and abstract properties. Egocentrism emerges in adolescents’ comparing reality to the ideal; thus, they often show idealistic thinking.

Piaget’s stages have been criticized on many grounds. One problem is that children often grasp ideas and are able to perform operations earlier than Piaget found. Another problem is that cognitive development across domains typically is uneven; rarely does a child think in stage-typical ways across all topics (e.g., mathematics, science, history). This also is true for adults; the same topic may be understood quite differently. For example, some adults may think of baseball in preoperational terms (“Hit the ball and run”), others might think of it concrete operationally (“What do I do in different situations?”), and some can reason using formal operational thought (e.g., “Explain why a curveball curves”). As a general framework, however, the stages describe the thought patterns that tend to co-occur, which is useful knowledge for educators, parents, and others who work with children.

Mechanisms of Learning.

Equilibration is an internal process (Duncan,  1995 ). As such, cognitive development can occur only when disequilibrium or cognitive conflict exists. An event must occur that produces a disturbance in the child’s cognitive structures so that the child’s beliefs do not match the observed reality. Equilibration seeks to resolve the conflict through assimilation and accommodation.

Piaget felt that development would proceed naturally through regular interactions with the physical and social environments. The impetus for developmental change is internal. Environmental factors are extrinsic; they can influence development but not direct it. This point has profound implications for education because it suggests that teaching may have little impact on development. Teachers can arrange the environment to cause conflict, but how any particular child resolves the conflict is not predictable.

Learning occurs, then, when children experience cognitive conflict and engage in assimilation or accommodation to construct or alter internal structures. Importantly, however, the conflict should not be too great because this will not trigger equilibration. Learning will be optimal when the conflict is small and especially when children are in transition between stages. Information must be partially understood (assimilated) before it can promote structural change (accommodation). Environmental stimulation to facilitate change should have negligible effect unless the critical stage transitions have begun so that the conflict can be successfully resolved via equilibration. Thus, learning is limited by developmental level (Brainerd,  2003 ).

The research evidence on cognitive conflict is not overwhelmingly supportive of Piaget’s position (Zimmerman & Blom,  1983a ,  1983b ; Zimmerman & Whitehurst,  1979 ). Rosenthal and Zimmerman ( 1978 ) summarized data from several research studies showing that preoperational children can master concrete operational tasks through teaching involving verbal explanations and modeled demonstrations. According to the theory, this should not happen unless the children are in stage transition, at which time cognitive conflict would be at a reasonable level.

The stagelike changes in children’s thinking seem to be linked to more gradual changes in attention and cognitive processing (Meece,  2002 ). Children may not demonstrate Piagetian stage understanding for various reasons, including not attending to the relevant stimuli, improperly encoding information, not relating information to prior knowledge, or using ineffective means to retrieve information (Siegler,  1991 ). When children are taught to use cognitive processes more effectively, they often can perform tasks at higher cognitive levels.

Piaget’s theory is constructivist because it assumes that children construct and then impose their concepts on the world to make sense of it. These concepts are not inborn; rather, children acquire them through their normal experiences. Information from the physical and social environments is not automatically received but rather is processed according to the child’s prevailing mental structures. Children make sense of their environments and construct reality based on their capabilities at the present time. With experience, these basic concepts develop into more sophisticated views.

Implications for Instruction

Piaget contended that cognitive development could not be taught, although research evidence shows that it can be accelerated (Zimmerman & Whitehurst,  1979 ). The theory and research have implications for instruction ( Table 8.3 ).

Understand Cognitive Development.

Teachers will benefit when they understand at what levels their students are functioning. All students in a class should not be expected to operate at the same level. Many Piagetian tasks are easy to administer (Wadsworth,  1996 ). Teachers can try to ascertain levels and gear their teaching accordingly. Students who seem to be in stage transition may benefit from teaching at the next higher level, because the conflict will not be too great for them.

Keep Students Active.

Piaget decried passive learning. Children need rich environments that allow for active exploration and hands-on activities. This arrangement facilitates active construction of knowledge.

Create Incongruity.

Development occurs only when environmental inputs do not match students’ cognitive structures. Material should not be readily assimilated but not too difficult to preclude accommodation. Incongruity also can be created by allowing students to solve problems and arrive at wrong answers. Nothing in Piaget’s theory says that children always have to succeed; teacher feedback indicating incorrect answers can promote disequilibrium.

Provide Social Interaction.

Although Piaget’s theory contends that development can proceed without social interaction, the social environment is nonetheless a key source for cognitive development. Activities that provide social interactions are useful. Learning that others have different points of view can help children become less egocentric.  Application 8.2  discusses ways that teachers can help to foster cognitive development.

APPLICATION 8.2 Piaget and Education

At all grades teachers should evaluate the developmental levels of their students prior to planning lessons. Teachers need to know how their students are thinking so they can introduce cognitive conflict at a reasonable level, where students can resolve it through assimilation and accommodation. Teachers at the early elementary levels, for example, are apt to have students who operate at both the preoperational and concrete operational levels, which means that one lesson will not suffice for any particular unit. Furthermore, because some children will grasp operations more quickly than others, teachers need to build enrichment activities into their lessons.

Teachers at later elementary and middle grades levels include lesson components that require basic understanding and also those that necessitate abstract reasoning. For example, they may incorporate activities that require factual answers, as well as activities that have no right or wrong answers but that require students to think abstractly and construct their ideas through reasoned judgments based on data. For students who are not fully operating at the formal operational level, the components requiring abstract reasoning may produce desired cognitive conflict and enhance a higher level of thinking. For students who already are operating at a formal operational level, the reasoning activities will continue to challenge them.

We now turn to Bruner’s theory of cognitive growth. This theory and Piaget’s are constructivist because they posit that people form or construct much of what they learn and understand.

BRUNER’S THEORY OF COGNITIVE GROWTH

Jerome Bruner’s theory of cognitive growth does not link changes in development with cognitive structures as Piaget’s does (Lutkehaus & Greenfield,  2003 ). Rather, Bruner’s theory highlights the various ways that children represent knowledge. The theory has implications for teaching and learning.

Knowledge Representation

According to Bruner ( 1964 ), “The development of human intellectual functioning from infancy to such perfection as it may reach is shaped by a series of technological advances in the use of mind” (p. 1). These technological advances depend on increasing language facility and exposure to systematic instruction (Bruner,  1966 ). As children develop, their actions are constrained less by immediate stimuli. Cognitive processes (e.g., thoughts, beliefs) mediate the relationship between stimulus and response so that learners can maintain the same response in a changing environment or perform different responses in the same environment, depending on what they consider adaptive.

People represent knowledge in three ways, which emerge in a developmental sequence: enactive, iconic, and symbolic (Bruner,  1964 ; Bruner, Olver, & Greenfield,  1966 ). These modes are not structures, but rather involve different forms of cognitive processing (i.e., functions;  Table 8.4 ).

Enactive representation  involves motor responses, or ways to manipulate the environment. Actions such as riding a bicycle and tying a knot are represented largely in muscular actions. Stimuli are defined by the actions that prompt them. Among toddlers, a ball (stimulus) is represented as something to throw and bounce (actions).

Table 8.4 Bruner’s modes of knowledge representation.

Iconic representation  refers to action-free mental images. Children acquire the capability to think about objects that are not physically present. They mentally transform objects and think about their properties separately from what actions can be performed with the objects. Iconic representation allows one to recognize objects.

Symbolic representation  uses symbol systems (e.g., language, mathematical notation) to encode knowledge. Such systems allow one to understand abstract concepts (e.g., the x variable in 3x − 5 = 10) and to alter symbolic information as a result of verbal instruction. Symbolic systems represent knowledge with remote and arbitrary features. The word “Philadelphia” looks no more like the city than a nonsense syllable (Bruner,  1964 ).

The symbolic mode is the last to develop and quickly becomes the preferred mode, although people maintain the capability to represent knowledge in the enactive and iconic modes. One might experience the feel of a tennis ball, form a mental picture of it, and describe it in words. The primary advantage of the symbolic mode is that it allows learners to represent and transform knowledge with greater flexibility and power than is possible with the other modes (Bruner,  1964 ).

Spiral Curriculum

That knowledge can be represented in different ways suggests that teachers should vary instruction depending on learners’ developmental levels. Before children can comprehend abstract mathematical notation, they can be exposed to mathematical concepts and operations represented enactively (with blocks) and iconically (in pictures). Bruner emphasized teaching as a means of prompting cognitive development. To say that a particular concept cannot be taught because students will not understand it really is saying that students will not understand the concept the way teachers plan to teach it. Instruction needs to be differentiated to match children’s cognitive capabilities.

Bruner ( 1960 ) is well known for his proposition that any content can be taught in meaningful fashion to learners of any age:

·  Experience over the past decade points to the fact that our schools may be wasting precious years by postponing the teaching of many important subjects on the ground that they are too difficult…. The foundations of any subject may be taught to anybody at any age in some form…. The basic ideas that lie at the heart of all science and mathematics and the basic themes that give form to life and literature are as simple as they are powerful. To be in command of these basic ideas, to use them effectively, requires a continual deepening of one’s understanding of them that comes from learning to use them in progressively more complex forms. It is only when such basic ideas are put in formalized terms as equations or elaborated verbal concepts that they are out of reach of the young child, if he has not first understood them intuitively and had a chance to try them out on his own. (pp. 12–13)

Bruner’s proposition has been misinterpreted to mean that learners of any age can be taught anything, which is not true. Bruner recommended that content be revisited: Concepts initially should be taught in a simple fashion so children can understand them and represented in a more complex fashion with development. Such revisiting creates a  spiral curriculum : We return to teaching the same concepts but the learning is different (e.g., more complex or nuanced). In literature, children may be able to understand intuitively the concepts of “comedy” and “tragedy” (e.g., “comedies are funny and tragedies are sad”) even though they cannot verbally describe them in literary terms. With development, students will read, analyze, and write papers on comedies and tragedies. Students should address topics at increasing levels of complexity as they move through the curriculum, rather than encountering a topic only once.

APPLICATION 8.3 Modes of Knowledge Representation

Bruner’s theory elaborates ways that students can represent knowledge and recommends revisiting learning through a spiral curriculum. A good application is found in mathematics. Before students can comprehend abstract mathematical notation, teachers must ensure that students understand the concepts enactively and iconically. Ms. Braxton, a third-grade teacher, works with second- and fourth-grade teachers as she prepares her math units for the year. She wants to ensure that students understand previous concepts before tackling new ones, and she introduces ideas that will be further developed during the next year. When introducing multiplication, she first reviews with her third graders addition and counting by multipliers (e.g., 2, 4, 6, 8; 4, 8, 12, 16). Then she has the students work with manipulatives (enactive representation), and she provides visual (iconic) representation of multiplication. Eventually she presents problems in symbolic mode (e.g., 4 × 2 = ?).

Ms. Cannon, a ninth-grade English teacher, examines curriculum guides and meets with middle school teachers to determine what material has been covered. As she develops units, she starts the first lesson with a review of the material that students studied previously and asks students to share what they can recall. Once she evaluates the mastery level of the students, she is able to build on the unit and add new material. She strives to employ all modes of knowledge representation in her teaching: enactive—role playing, dramatization; iconic—pictures, videos; symbolic—print materials, websites.

Bruner’s theory is constructivist because it assumes that at any age learners assign meaning to stimuli and events based on their cognitive capabilities and experiences with the social and physical environments. Bruner’s modes of representation bear some similarity to the operations that students engage in during Piaget’s stages (i.e., sensorimotor—enactive, concrete operational—iconic, formal operational—symbolic), although Bruner’s is not a stage theory. Bruner’s theory also allows for concepts to be mentally represented in multiple modes simultaneously: An adolescent knows how to throw a basketball, can visualize its appearance, and can compute its circumference with the formula c = πd.  Application 8.3  gives some examples of Bruner’s ideas applied to teaching and learning.

VYGOTSKY’S SOCIOCULTURAL THEORY

Vygotsky’s theory, like Piaget’s, is constructivist; however, Vygotsky’s places more emphasis on the social environment as a facilitator of development and learning (Tudge & Scrimsher,  2003 ). The background of the theory is discussed, along with its key assumptions and principles.

Background

Lev Semenovich Vygotsky, who was born in Russia in 1896, studied various subjects in school, including psychology, philosophy, and literature, and received a law degree from Moscow Imperial University in 1917. Following graduation, he returned to his hometown of Gomel, which was beset with problems stemming from German occupation, famine, and civil war. Two of his brothers died, and he contracted tuberculosis—the disease that eventually killed him. He taught courses in psychology and literature, wrote literary criticism, and edited a journal. He also worked at a teacher training institution, where he founded a psychology laboratory and wrote an educational psychology book (Tudge & Scrimsher,  2003 ).

A critical event occurred in 1924 at the Second All-Russian Congress of Psychoneurology in Leningrad. Prevailing psychological theory at that time neglected subjective experiences in favor of Pavlov’s conditioned reflexes and behaviorism’s emphasis on environmental influences. Vygotsky presented a paper (“The Methods of Reflexological and Psychological Investigation”) in which he criticized the dominant views and spoke on the relation of conditioned reflexes to human consciousness and behavior. Pavlov’s experiments with dogs ( Chapter 3 ) and Köhler’s studies with apes ( Chapter 7 ) erased many distinctions between animals and humans.

Vygotsky contended that, unlike animals that react only to the environment, humans have the adaptive capacity to alter the environment for their own purposes. His speech made such an impression on one listener—Alexander Luria (discussed later in this chapter)—that he was invited to join the prestigious Institute of Experimental Psychology in Moscow. He helped to establish the Institute of Defektology, whose purpose was to determine ways to help handicapped individuals. Until his death in 1934, he wrote extensively on the social mediation of learning and the role of consciousness, often in collaboration with colleagues Luria and Leontiev (Rohrkemper,  1989 ).

Understanding Vygotsky’s position requires keeping in mind that he was a Marxist and that his views represented an attempt to apply marxist ideas of social change to language and development (Rohrkemper,  1989 ). After the 1917 Russian Revolution, an urgency among the new leaders produced rapid change in the populace. Vygotsky’s strong sociocultural theoretical orientation fit well with the revolution’s goals of changing the culture to a socialist system.

Vygotsky had some access to Western society (e.g., writers such as Piaget; Bredo,  1997 ; Tudge & Winterhoff,  1993 ), but little of what he wrote was published during his lifetime or for some years following his death (Gredler,  2009 ). A negative political climate prevailed in the former Soviet Union; among other things, the Communist Party curtailed psychological testing and publications. Vygotsky espoused revisionist thinking (Bruner,  1984 ). He moved from a Pavlovian view of psychology focusing on reflexes to a cultural–historical perspective that stressed language and social interaction (Tudge & Scrimsher,  2003 ). Some of his writings were at odds with Stalin’s views and because of that were not published. References to his work were banned in the Soviet Union until the 1980s (Tudge & Scrimsher,  2003 ). In recent years, Vygotsky’s writings have been increasingly translated and circulated, which has expanded their impact on such disciplines as education, psychology, and linguistics.

Basic Principles

One of Vygotsky’s central contributions to psychological thought was his emphasis on socially meaningful activity as an important influence on human consciousness (Bredo,  1997 ; Gredler,  2012 ; Kozulin,  1986 ; Tudge & Winterhoff,  1993 ). Vygotsky attempted to explain human thought in new ways. He rejected introspection ( Chapter 1 ) and raised many of the same objections as the behaviorists. He wanted to abandon explaining states of consciousness by referring to the concept of consciousness; similarly, he rejected behavioral explanations of action in terms of prior actions. Rather than discarding consciousness (which the behaviorists did) or the role of the environment (which the introspectionists did), he sought a middle ground of taking environmental influence into account through its effect on consciousness.

Vygotsky’s theory stresses the interaction of interpersonal (social), cultural–historical, and individual factors as the key to human development (Tudge & Scrimsher,  2003 ). Interactions with persons in the environment (e.g., apprenticeships, collaborations) stimulate developmental processes and foster cognitive growth. But interactions are not useful in a traditional sense of providing children with information. Rather, children transform their experiences based on their knowledge and characteristics and reorganize their mental structures.

The cultural–historical aspects of Vygotsky’s theory illuminate the point that learning and development cannot be dissociated from their context. The way that learners interact with their worlds—with the persons, objects, and institutions in it—transforms their thinking. The meanings of concepts change as they are linked with the world (Gredler,  2009 ). Thus, “school” is not simply a word or a physical structure but also an institution that seeks to promote learning and citizenship.

There also are individual, or inherited, factors that affect development. Vygotsky was interested in children with mental and physical disabilities. He believed that their inherited characteristics produced learning trajectories different from those of children without such challenges.

Of these three influences, the one that has received the most attention—at least among Western researchers and practitioners—is the interpersonal. Vygotsky considered the social environment critical for learning and thought that social interactions transformed learning experiences. Social activity is a phenomenon that helps explain changes in consciousness and establishes a psychological theory that unifies behavior and mind (Kozulin,  1986 ; Wertsch,  1985 ).

The social environment influences cognition through its  tools —that is, its cultural objects (e.g., cars, machines) and its language and social institutions (e.g., schools, churches). Social interactions help to coordinate the three influences on development. Cognitive change results from using cultural tools in social interactions and from internalizing and mentally transforming these interactions (Bruning et al.,  2011 ). Vygotsky’s position is a form of dialectical (cognitive) constructivism because it emphasizes the interaction between persons and their environments.  Mediation  is the key mechanism in development and learning:

·  All human psychological processes (higher mental processes) are mediated by such psychological tools as language, signs, and symbols. Adults teach these tools to children in the course of their joint (collaborative) activity. After children internalize these tools they function as mediators of the children’s more advanced psychological processes. (Karpov & Haywood,  1998 , p. 27)

Vygotsky’s most controversial contention was that all higher mental functions originated in the social environment (Vygotsky,  1962 ). This is a powerful claim, but it has a good degree of truth to it. The most influential process involved is language. Vygotsky thought that a critical component of psychological development was mastering the external process of transmitting cultural development and thinking through symbols such as language, counting, and writing. Once this process was mastered, the next step involved using these symbols to influence and self-regulate thoughts and actions. Self-regulation uses the important function of private speech (discussed later in this chapter).

In spite of this impressive theorizing, Vygotsky’s claim appears to be too strong. Research evidence shows that young children mentally figure out much knowledge about the way the world operates long before they have an opportunity to learn from the culture in which they live (Bereiter,  1994 ). Children also seem biologically predisposed to acquire certain concepts (e.g., understanding that adding increases quantity), which does not depend on the environment (Geary,  1995 ). Although social learning affects knowledge construction, the claim that all learning derives from the social environment seems overstated. Nonetheless, we know that learners’ cultures are critical and need to be considered in explaining learning and development. A summary of major points in Vygotsky’s ( 1978 ) theory appears in  Table 8.5  (Meece,  2002 ).

Zone of Proximal Development

A key concept in Vygotsky’s theory is the  zone of proximal development (ZPD) , defined as “the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers” (Vygotsky,  1978 , p. 86). The ZPD represents the amount of learning possible by a student given the proper instructional conditions (Puntambekar & Hübscher,  2005 ). It is largely a test of a student’s developmental readiness or intellectual level in a specific domain. The ZPD shows how learning and development are related (Bredo,  1997 ; Campione, Brown, Ferrara, & Bryant,  1984 ), and it can be viewed as an alternative to the conception of intelligence (Belmont,  1989 ). In the ZPD, a teacher and learner (adult/child, tutor/tutee, model/observer, master/apprentice, expert/novice) work together on a task that the learner could not perform independently because of the difficulty level (Gredler,  2012 ). The ZPD reflects the Marxist idea of collective activity, in which those who know more or are more skilled share that knowledge and skill to accomplish a task with those who know less (Bruner,  1984 ).

Cognitive change occurs in the ZPD as teacher and learner share cultural tools, and this culturally mediated interaction produces cognitive change when it is internalized in the learner (Cobb,  1994 ). Working in the ZPD requires a good deal of guided participation (Rogoff,  1986 ); however, children do not acquire cultural knowledge passively from these interactions, nor is what they learn necessarily an automatic or accurate reflection of events. Rather, learners bring their own understandings to social interactions and construct meanings by integrating those understandings with their experiences in the context. The learning often is sudden, in the Gestalt sense of insight ( Chapter 7 ), rather than reflecting a gradual accretion of knowledge (Wertsch,  1984 ).

For example, assume that a teacher (Trudy) and a child (Laura) will work on a task (making a picture of mom, dad, and Laura doing something together at home). Laura brings to the task her understandings of what the people and the home look like and of the types of things they might work on, combined with knowledge of how to draw and make pictures. Trudy brings the same understandings plus knowledge of conditions necessary to work on various tasks. Suppose they decide to make a picture of the three working in the yard. Laura might draw a picture of dad cutting grass, mom trimming shrubs, and Laura raking the lawn. If Laura were to draw herself in front of dad, Trudy would explain that Laura must be behind dad to rake up the grass left behind by dad’s cutting. During the interaction, Laura modifies her beliefs about working in the yard based on her current understanding and on the new knowledge she constructs.

Despite the importance of the ZPD, the overarching emphasis it has received in Western cultures has served to distort its meaning and downplay the complexity of Vygotsky’s theory (Gredler,  2012 ).

·  Moreover, the concept itself has too often been viewed in a rather limited way that emphasized the interpersonal at the expense of the individual and cultural-historical levels and treats the concept in a unidirectional fashion. As if the concept were synonymous with “scaffolding,” too many authors have focused on the role of the more competent other, particularly the teacher, whose role is to provide assistance just in advance of the child’s current thinking…. The concept thus has become equated with what sensitive teachers might do with their children and has lost much of the complexity with which it was imbued by Vygotsky, missing both what the child brings to the interaction and the broader setting (cultural and historical) in which the interaction takes place. (Tudge & Scrimsher,  2003 , p. 211)

The influence of the cultural-historical setting is seen clearly in Vygotsky’s belief that schooling was important not because it was where children were scaffolded but, rather, because it allowed them to develop greater awareness of themselves, their language, and their role in the world order. Participating in the cultural world transforms mental functioning rather than simply accelerating processes that would have developed anyway. Broadly speaking, the ZPD refers to new forms of awareness that occur as people interact with their societies’ social institutions. The culture affects the course of one’s mental development. It is unfortunate that in most discussions of the ZPD, it is conceived so narrowly (Gredler,  2012 ); namely, as an expert teacher providing learning opportunities for a student (although that is part of it).

Applications

Vygotsky’s ideas lend themselves to many educational applications (Karpov & Haywood,  1998 ; Moll,  2001 ). The field of self-regulated learning ( Chapter 10 ) has been strongly influenced by the theory. Self-regulated learning requires metacognitive processes such as planning, checking, and evaluating. This section and  Application 8.4  discuss other examples.

APPLICATION 8.4 Applying Vygotsky’s Theory

Vygotsky postulated that one’s interactions with the environment assist learning. The experiences one brings to a learning situation can greatly influence the outcome.

Ice skating coaches may work with advanced students who have learned a great deal about ice skating and how they perform on the ice. Students bring with them their concepts of balance, speed, movement, and body control based on their experiences skating. Coaches take the strengths and weaknesses of these students and help them learn to alter various movements to improve their performances. For example, a skater who has trouble completing a triple axel toe loop has the height and speed needed to complete the jump, but the coach notices that she turns her toe at an angle during the spin that alters the smooth completion of the loop. After the coach points this out to the skater and helps her learn to alter that movement, she is able to successfully complete the jump.

Veterinary students who have grown up on farms and have experienced births, illnesses, and care of various types of animals bring valuable knowledge to their training. Veterinary instructors can use these prior experiences to enhance students’ learning. In teaching students how to treat an injured hoof of a cow or horse, the instructor might call on some of these students to discuss what they have observed and then build on that knowledge by explaining the latest and most effective methods of treatment.

Helping students acquire cognitive mediators (e.g., signs, symbols) through the social environment can be accomplished in many ways. A common application involves the concept of  instructional scaffolding , which refers to the process of controlling task elements that are beyond the learners’ capabilities so that they can focus on and master those features of the task that they can grasp quickly (Puntambekar & Hübscher,  2005 ). To use an analogy of scaffolding employed in construction projects, instructional scaffolding has five major functions: provide support, function as a tool, extend the range of the learner, permit the attainment of tasks not otherwise possible, and use selectively only as needed.

In a learning situation, a teacher initially might take the lead, after which the teacher and the learners share responsibility. As learners become more competent, the teacher gradually withdraws the scaffolding so learners can perform independently (Campione et al.,  1984 ). The key is to ensure that the scaffolding keeps learners in the ZPD, which is raised as they develop capabilities. Students are challenged to learn within the bounds of the ZPD. We see in the opening lesson how Ali and the others were able to learn given the proper instructional support.

It is critical to understand that scaffolding is not a formal part of Vygotsky’s theory (Puntambekar & Hübscher,  2005 ). The term was coined by Wood, Bruner, and Ross ( 1976 ). It does, however, fit nicely within the ZPD.  Scaffolding  is part of Bandura’s ( 1986 ) participant modeling technique ( Chapter 4 ), in which a teacher initially models a skill, provides support, and gradually reduces aid as learners develop the skill. The notion also bears some relation to shaping ( Chapter 3 ), as instructional supports are used to guide learners through various stages of skill acquisition.

Scaffolding is appropriate when a teacher wants to provide students with some information or to complete parts of tasks for them so that they can concentrate on the part of the task they are attempting to master. A teacher assisting children with organizing sentences in a paragraph to express ideas in a logical order initially might give them the sentences with word meanings and spellings so that these needs would not interfere with their primary task. As they became more competent in sequencing ideas, the teacher might have students compose their own paragraphs while still assisting with word meanings and spellings. Eventually students will assume responsibility for these functions. In short, the teacher creates a ZPD and provides the scaffolding for students to be successful (Moll,  2001 ).

Another application that reflects Vygotsky’s ideas is  reciprocal teaching  ( Chapter 7 ). Reciprocal teaching involves an interactive dialogue between a teacher and small group of students. Initially the teacher models the activities, after which teacher and students take turns being the teacher. If students are learning to ask questions during reading comprehension, the instructional sequence might include the teacher modeling a question-asking strategy for determining level of understanding. From a Vygotskian perspective, reciprocal teaching comprises social interaction and scaffolding as students gradually develop skills.

An important application area is  peer collaboration , which reflects the notion of collective activity (Bruner,  1984 ; Ratner et al.,  2002 ; see section on peer-assisted learning later in this chapter). When peers work on tasks cooperatively, the shared social interactions can serve an instructional function. Research shows that cooperative groups are most effective when each student has assigned responsibilities and all must attain competence before any are allowed to progress (Slavin,  1995 ). Peer groups are commonly used for learning in fields such as mathematics, science, and language arts (Cobb,  1994 ; Cohen,  1994 ; DiPardo & Freedman,  1988 ; Geary,  1995 ; O’Donnell,  2006 ), which attests to the recognized impact of the social environment during learning.

An application relevant to Vygotsky’s theory and to situated cognition is social guidance through  apprenticeships  (Radziszewska & Rogoff,  1991 ; Rogoff,  1990 ). In apprenticeships, novices work closely with experts in joint work-related activities. Apprenticeships fit well with the ZPD because they occur in cultural institutions (e.g., schools, agencies) and thus help to transform learners’ cognitive development. On the job, apprentices operate within a ZPD because they often work on tasks beyond their capabilities. By working with experts, novices develop a shared understanding of important processes and integrate this with their current understandings. Apprenticeships represent a type of dialectical constructivism that depends heavily on social interactions.

Apprenticeships are used in many areas of education (Bailey,  1993 ). Student teachers work with cooperating teachers in schools and, once on the job, often are paired with experienced teachers for mentoring. Students conduct research with and are mentored by professors (Mullen,  2005 ). Counselor trainees serve internships under the direct guidance of a supervisor. On-the-job training programs use the apprentice model as students acquire skills while in the actual work setting and interacting with others. Future research should evaluate the factors that influence the success of apprenticeships as a means of fostering skill acquisition in students of various ages.

Many theorists contend that constructivism (and Vygotsky’s theory in particular) represents a viable model for explaining how mathematics is learned (Ball, Lubienski, & Mewborn,  2001 ; Cobb,  1994 ; Lampert,  1990 ). Mathematical knowledge is not passively absorbed from the environment, but rather is constructed by individuals as a consequence of their interactions. This construction process also includes children’s inventing of procedures that incorporate implicit rules.

The following unusual example illustrates rule-based procedural invention. Some time ago I was working with a teacher to identify children in her class who might benefit from additional instruction in long division. She named several students and said that Tim also might qualify, but she was not sure. Some days he worked his problems correctly, whereas other days his work was incorrect and made no sense. I gave him problems to solve and asked him to verbalize while working because I was interested in what children thought about while they solved problems. This is what Tim said: “The problem is 17 divided into 436. I start on the side of the problem closest to the door…” I then knew why on some days his work was accurate and on other days it was not. It depended on which side of his body was closest to the door!

The process of constructing knowledge begins in the preschool years (Resnick,  1989 ). Geary ( 1995 ) distinguished biologically primary (biologically based) from biologically secondary (culturally taught) abilities. Biologically primary abilities are grounded in neurobiological systems that have evolved in particular ecological and social niches and that serve functions related to survival or reproduction. They should be seen cross-culturally, whereas biologically secondary abilities should show greater cultural specificity (e.g., as a function of schooling). Furthermore, many of the former should be seen in very young children. Indeed, counting is a natural activity that preschoolers do without direct teaching (Resnick,  1985 ). Even infants may be sensitive to different properties of numbers (Geary,  1995 ).

Mathematical competence also depends on sociocultural influence (Cobb,  1994 ). Vygotsky ( 1978 ) stressed the role of competent other persons in the ZPD. The sociocultural influence is incorporated through such activities as peer teaching, instructional scaffolding, and apprenticeships.

Research supports the idea that social interactions are beneficial. Rittle-Johnson and Star ( 2007 ) found that seventh graders’ mathematical proficiency was enhanced when they were allowed to compare solution methods with partners. Results of a literature review by Springer, Stanne, and Donovan ( 1999 ) showed that small-group learning significantly raised college students’ achievement in mathematics and science. Kramarski and Mevarech ( 2003 ) found that combining cooperative learning with metacognitive instruction (e.g., reflect on relevant concepts, decide on appropriate strategies to use) raised eighth graders’ mathematical reasoning more than either procedure alone. In addition to these benefits of cooperative learning (Stein & Carnine,  1999 ), the literature on peer and cross-age tutoring in mathematics reveals that it is effective in raising children’s achievement (Robinson, Schofield, & Steers-Wentzell,  2005 ).

Despite its popularity and potential for application, it is difficult to evaluate the contributions of Vygotsky’s ( 1978 ,  1987 ) theory to human development and learning (Tudge & Scrimsher,  2003 ). Researchers and practitioners have tended to focus on the ZPD without placing it in a larger theoretical context that is centered around cultural influence. When applications of Vygotsky’s theory are discussed, they often are not part of the theory, but rather seem to fit with it. When Wood et al. ( 1976 ) introduced the term  scaffolding , for example, they presented it as a way for teachers to structure learning environments. As such, it has little relation to the dynamic ZPD that Vygotsky wrote about. Although  reciprocal teaching also is not a Vygotskian concept, the term captures much better this sense of dynamic, multidirectional interaction.

Debate over the theory often has focused on “Piaget versus Vygotsky,” contrasting their presumably discrepant positions on the course of human development, although on many points they do not differ (Duncan,  1995 ). While such debates may illuminate differences and provide testable research hypotheses, they are not helpful to educational practitioners seeking ways to help children learn.

Possibly the most significant implication of Vygotsky’s theory for education is that the cultural–historical context is relevant to all forms of learning because learning does not occur in isolation. Student–teacher interactions are part of that context. Research has identified, for example, different interaction styles between Hawaiian, Anglo, and Navajo children (Tharp,  1989 ; Tharp & Gallimore,  1988 ). Whereas the Hawaiian culture encourages collaborative activity and more than one student talking at once, Navajo children are less acculturated to working in groups and more likely to wait to talk until the speaker is finished. Thus, the same instructional style would not be equally beneficial for all cultures. This point is especially noteworthy given the large influx of English language learners in U.S. schools. Being able to differentiate instruction to fit children’s learning preferences is a key 21st-century skill.

PRIVATE SPEECH AND SOCIALLY MEDIATED LEARNING

A central premise of constructivism is that learning involves transforming and internalizing the social environment. This section discusses the pivotal roles of private speech and socially mediated learning.

Private Speech

Private speech  refers to the set of speech phenomena that has a self-regulatory function but is not socially communicative (Fuson,  1979 ). Various theories—including constructivism, cognitive-developmental, and social cognitive—establish a strong link between private speech and the development of self-regulation (Berk,  1986 ; Frauenglass & Diaz,  1985 ).

The historical impetus derives in part from work by Pavlov ( 1927 ). Recall from  Chapter 3  that Pavlov distinguished the  first signal system  (perceptual) from the second (linguistic). Pavlov realized that principles of animal conditioning do not completely generalize to humans; human conditioning often occurs quickly with one or a few pairings of conditioned stimulus and unconditioned stimulus, in contrast to the multiple pairings required with animals. Pavlov believed that conditioning differences between humans and animals are largely due to the human capacity for language and thought. Stimuli may not produce conditioning automatically; people interpret stimuli in light of their prior experiences. Although Pavlov did not conduct research on the second signal system, subsequent investigations have validated his beliefs that human conditioning is complex and language plays a mediating role.

The Soviet psychologist Luria ( 1961 ) focused on the child’s transition from the first to the second signal system. Luria postulated three stages in the development of verbal control of motor behavior. Initially, the speech of others is primarily responsible for directing the child’s behavior (ages 1½ to 2½). During the second stage (ages 3 to 4), the child’s overt verbalizations initiate motor behaviors but do not necessarily inhibit them. In the third stage, the child’s private speech becomes capable of initiating, directing, and inhibiting motor behaviors (ages 4½ to 5½). Luria believed this private, self-regulatory speech directs behavior through neurophysiological mechanisms.

The mediating and self-directing role of the second signal system is embodied in Vygotsky’s theory. Vygotsky ( 1962 ) believed private speech helps develop thought by organizing behavior. Children employ private speech to understand situations and surmount difficulties. Private speech occurs in conjunction with children’s interactions in the social environment. As children’s language facility develops, words spoken by others acquire meaning independent of their phonological and syntactical qualities. Children internalize word meanings and use them to direct their behaviors.

Vygotsky hypothesized that private speech follows a curvilinear developmental pattern: Overt verbalization (thinking aloud) increases until ages 6 to 7, after which it declines and becomes primarily covert (internal) by ages 8 to 10. However, overt verbalization can occur at any age when people encounter problems or difficulties. Research shows that although the amount of private speech decreases from approximately ages 4 or 5 to 8, the proportion of private speech that is self-regulating and goal directed increases with age (Winsler, Carlton, & Barry,  2000 ). In many research investigations, the actual amount of private speech is small, and many children do not verbalize at all. Thus, the developmental pattern of private speech seems more complex than originally hypothesized by Vygotsky.

Verbalization and Achievement

Verbalization of rules, procedures, and strategies can improve student learning. Although Meichenbaum’s ( 1977 ,  1986 )  self-instructional training  procedure ( Chapter 4 ) is not rooted in constructivism, it re-creates the overt-to-covert developmental progression of private speech. Types of statements modeled are problem definition (“What is it I have to do?”), focusing of attention (“I need to pay attention to what I’m doing”), planning and response guidance (“I need to work carefully”),  self-reinforcement  (“I’m doing fine”),  self-evaluation  (“Am I doing things in the right order?”), and  coping  (“I need to try again when I don’t get it right”). Teachers can use self-instructional training to teach learners cognitive and motor skills, and it can result in creating a positive task outlook and fostering perseverance in the face of difficulties (Meichenbaum & Asarnow,  1979 ). The procedure need not be scripted; learners can construct their own verbalizations.

Verbalization seems beneficial for students who often experience difficulties and perform in a deficient manner. Positive results have been obtained with children who do not spontaneously rehearse material to be learned, impulsive learners, students with learning disabilities and mental retardation, and learners who require remedial experiences (Schunk,  1986 ). Verbalization helps students with learning problems work at tasks systematically (Hallahan, Kneedler, & Lloyd,  1983 ). It forces students to attend to tasks and rehearse content to be learned. Verbalization does not seem to facilitate learning when students can handle task demands adequately without verbalizing. Because verbalization constitutes an additional task, it might interfere with learning by distracting children from the task at hand.

Berk ( 1986 ) studied first and third graders’ spontaneous private speech. Task-relevant overt speech was negatively related, and faded verbalization (whispers, lip movements, muttering) was positively related to mathematical performance. These results were obtained for first graders of high intelligence and third graders of average intelligence; among third graders of high intelligence, overt and faded speech showed no relationship to achievement. For the latter students, internalized self-guiding speech apparently is the most effective. Daugherty and White ( 2008 ) found that private speech related positively to indexes of creativity among Head Start and low socioeconomic status preschoolers.

Keeney, Cannizzo, and Flavell ( 1967 ) pretested 6- and 7-year-olds on a serial recall task and identified those who failed to rehearse prior to recall. After these children learned how to rehearse, their recall matched that of spontaneous rehearsers. Meichenbaum and Asarnow ( 1979 ) identified kindergartners who did not spontaneously rehearse on a serial recall test. Some were trained to use a rehearsal strategy similar to that of Keeney et al., whereas others received self-instructional training. Both treatments facilitated recall relative to a control condition, but the self-instructional treatment was more effective.

Schunk ( 1982b ) instructed students who lacked division skills. Some students verbalized explicit statements (e.g., “check,” “multiply,” “copy”), others constructed their own verbalizations, a third group verbalized the statements and their own verbalizations, and students in a fourth condition did not verbalize. Self-constructed verbalizations—alone or combined with the statements—led to the highest division skill.

In summary, verbalization is more likely to promote student achievement if it is relevant to the task and does not interfere with performance. Higher proportions of task-relevant statements produce better learning (Schunk & Gunn,  1986 ). Private speech follows an overt-to-covert developmental cycle, and speech becomes internalized earlier in students with higher intelligence (Berk,  1986 ; Frauenglass & Diaz,  1985 ). Private speech relates positively to creativity. Allowing students to construct their verbalizations—possibly in conjunction with verbalizing steps in a strategy—is more beneficial than limiting verbalizing to specific statements. To facilitate transfer and maintenance, overt verbalization should eventually be faded to whispering or lip movements and then to a covert level. Internalization is a key feature of self-regulated learning (Schunk,  1999 ;  Chapter 10 ).

These benefits of verbalization do not mean that all students ought to verbalize while learning. That practice would result in a loud classroom and would distract many students! Rather, verbalization could be incorporated into instruction for students having difficulties learning. A teacher or classroom aide could work with such students individually or in groups to avoid disrupting the work of other class members.  Application 8.5  discusses ways to integrate verbalization into learning.

APPLICATION 8.5 Self-Verbalization

A teacher might use self-verbalization (self-talk) in a special education resource room or in a regular classroom to assist students having difficulty attending to material and mastering skills. When introducing long division, a teacher might use verbalization to help children who cannot remember the steps to complete the procedure. Children can verbalize and apply the following steps:

· ■ Will (number) go into (number)?

· ■ Divide.

· ■ Multiply: (number) × (number) = (number).

· ■ Write down the answer.

· ■ Subtract: (number) − (number) = (number).

· ■ Bring down the next number.

· ■ Repeat steps.

Use of self-talk helps students stay on task and builds their self-efficacy to work systematically. Once they begin to grasp the content, it is to their advantage to fade verbalizations to a covert (silent) level so they can work more rapidly.

Self-verbalization also can help students who are learning sport skills and strategies. They might verbalize what is happening and what moves they should make. A tennis coach, for example, might encourage students to use self-talk during practice matches: “high ball—overhand return,” “low ball—underhand return,” “cross ball—backhand return.”

Aerobic and dance instructors could use self-talk during practice. A ballet teacher might have young students repeat “paint a rainbow” for a flowing arm movement, and “walk on eggs” to get them to move lightly on their toes. Participants in aerobic exercise classes also might verbalize movements (e.g., “bend and stretch,” “slide right and around”) as they perform them.

Socially Mediated Learning

Many forms of constructivism, and Vygotsky’s theory in particular, stress the idea that learning is a socially mediated process. This focus is not unique to constructivism; many other learning theories emphasize social processes as having a significant impact on learning. Bandura’s ( 1986 ,  1997 ) social cognitive theory ( Chapter 4 ), for example, highlights the reciprocal relations among learners and social environmental influences, and much research has shown that social modeling is a powerful influence on learning (Rosenthal & Zimmerman,  1978 ; Schunk,  1987 ). In Vygotsky’s theory, however, social mediation of learning is the central construct (Karpov & Haywood,  1998 ; Moll,  2001 ; Tudge & Scrimsher,  2003 ). All learning is mediated by tools such as language, symbols, and signs. Children acquire these tools during their social interactions with others. They internalize these tools and then use them as mediators of more advanced learning (i.e., higher cognitive processes such as concept learning and problem solving).

As an example, let us examine how social mediation influences concept acquisition. Young children acquire concepts spontaneously by observing their worlds and formulating hypotheses. For example, they hear the noise that cars make and the noise that trucks make, and they may believe that bigger objects make more noise. They have difficulty accommodating discrepant observations (e.g., a motorcycle is smaller than a car or truck but may make more noise than either).

Through social interactions, children are taught concepts by others (e.g., teachers, parents, older siblings). This often occurs directly, as when teachers teach children the difference between squares, rectangles, triangles, and circles. Children use the tools of language and symbols to internalize these concepts.

It is, of course, possible to learn on one’s own without social interactions. For example, Wirkala and Kuhn ( 2011 ) investigated problem-based learning among middle school students. Some learners worked individually, whereas others participated in small groups. The results showed that problem-based learning led to higher achievement compared with a lecture-discussion condition, but the problem-based learning individual and group conditions did not differ. Thus, the opportunity for socially mediated learning did not lead to greater benefits.

But even such independent learning is, in a constructivist sense, socially mediated, because it involves the tools (i.e., language, signs, symbols) that have been acquired through previous social interactions. Further, a certain amount of labeling is needed. Children may learn a concept but not have a name for it (“What do you call a thing that looks like ———?”). The label involves language and likely will be supplied by another person.

A central premise of contemporary learning theories that reflects constructivism is that people construct  implicit theories  about their environments and revise those theories as they encounter new evidence. Beginning at an early age, children construct theories of their minds and those of others, along with their understanding of the physical and biological worlds (Gopnik & Wellman,  2012 ). Children’s learning and thinking occur in the context of these implicit theories.

Social interactions are critical for children’s cognitive development. Children may not simply build propositional networks based on experience. Their understandings are situated in their theories of the world and include beliefs about the usefulness and importance of knowledge, how it relates to what else they know, and in what situations it may be appropriate. Cultural tools are essential for promoting the development of children’s implicit theories and understandings.

Tools are useful not only for learning but also for teaching. Children teach one another things they have learned. Vygotsky ( 1962 ,  1978 ) believed that by being used for social purposes, tools exert powerful influences on others.

These points suggest that preparation is needed for children to effectively construct knowledge. The teaching of the basic tools to learn can be direct. There is no need for students to construct the obvious or what they can be easily taught. Constructed discoveries are the result of basic learning, not their cause (Karpov & Haywood,  1998 ). Teachers should prepare students to learn by teaching them the tools and then providing opportunities for learning. Applications of socially mediated learning are discussed in  Application 8.6 .

APPLICATION 8.6 Socially Mediated Learning

Socially mediated learning is appropriate for students of all ages. Teacher education faculty members know that success in teaching depends in part on understanding the cultures of the communities served by schools. Dr. Mayer obtains consent from the schools where her students are placed and from the parents, and she assigns each student to be a “buddy” of a schoolchild. As part of their placements, her students spend extra time with their buddies—for example, working one-to-one, eating lunch with them, riding home on the school bus with them, and visiting them in their homes. She pairs her students, and the members of each dyad meet regularly to discuss the culture of their assigned buddies, such as what their buddies like about school, what their parents or guardians do, and characteristics of the neighborhoods where their buddies live. She meets regularly with each dyad to discuss the implications of the cultural variables for school learning. Through social interactions with buddies, Dr. Mayer, and other class members, the students develop a better understanding of the role of culture in schooling.

Historical events typically are open to multiple interpretations. As part of a unit on post–World War II changes in American life, Ms. Schmitz organizes students into five teams. Each team is assigned a topic: medicine, transportation, education, technology, suburbs. Teams prepare a presentation on why their topic represents a significant advance in American life. Students on each team work together to prepare the presentation, and each member presents part of it. After the presentations are finished, Ms. Schmitz leads a discussion with the class. She tries to get them to see how advances are interrelated: for example, technology influences medicine, transportation, and education; more automobiles and roads lead to growth in suburbs; and better education results in preventive medicine. Social mediation through discussions and presentations helps students gain a deeper understanding of changes in American life.

Peer-Assisted Learning

Peer-assisted learning methods fit well with constructivism. Peer-assisted learning refers to instructional approaches in which peers serve as active agents in the learning process (Rohrbeck, Ginsburg-Block, Fantuzzo, & Miller,  2003 ). Methods emphasizing peer-assisted learning include peer tutoring ( Chapter 4 and this section), reciprocal teaching ( Chapter 7 ), and cooperative learning (covered in this section; Palincsar & Brown,  1984 ; Slavin,  1995 ; Strain, Kerr, & Ragland,  1981 ).

Peer-assisted learning has been shown to promote achievement. In their review of the literature, Rohrbeck et al. ( 2003 ) found that peer-assisted learning was most effective with younger (first through third graders), urban, low-income, and minority children. These are promising results, given the risk to academic achievement associated with urban, low-income, and minority students. Rohrbeck et al. did not find significant differences due to content area (e.g., reading, mathematics). In addition to the learning benefits, peer-assisted learning also can foster academic and social motivation for learning (Ginsburg-Block, Rohrbeck, & Fantuzzo,  2006 ; Rohrbeck et al.,  2003 ). Peers who stress academic learning convey its importance, which then can motivate others in the social environment.

As with other instructional models, teachers need to consider the desired learning outcomes in determining whether peer-assisted learning should be used. Some types of lessons (e.g., those emphasizing inquiry skills) would seem to be ideally suited for this approach, and especially if the development of social outcomes also is an objective.

Peer Tutoring.

Peer tutoring  captures many of the principles of constructivist teaching. Students are active in the learning process; tutor and tutee freely participate. The one-to-one context may encourage tutees to ask questions that they might be reluctant to ask in a large class. There is evidence that peer tutoring can lead to greater achievement gains than traditional instruction (Fuchs, Fuchs, Mathes, & Simmons,  1997 ).

Peer tutoring also encourages cooperation among students and helps to diversify the class structure. A teacher might split the class into small groups and tutoring groups while continuing to work with a different group. The content of the tutoring is tailored to the specific needs of the tutee.

Teachers likely will need to instruct peer tutors to ensure that they possess the requisite academic and tutoring skills. It also should be clear what the tutoring session is expected to accomplish. A specific goal is preferable to a general one—thus, “Work with Mike to help him understand how to regroup from the 10s column,” rather than “Work with Mike to help him get better in subtraction.”

Cooperative Learning.

Cooperative learning  is a form of socially mediated learning that is frequently used in classrooms (Slavin,  1994 ,  1995 ), but when not properly structured can lead to poorer learning compared with whole-class instruction. In cooperative learning the objective is to develop in students the ability to work collaboratively with others. The task should be one that is too extensive for a single student to complete in a timely fashion. The task also should lend itself well to a group, such as by having components that can be completed by individual students who then merge their individual work into a final product.

There are certain principles that help cooperative groups be successful. One is to form groups with students who are likely to work together well and who can develop and practice cooperative skills. This does not necessarily mean allowing students to choose groups, since they may select their friends, and some students may be left without a group. It also does not necessarily mean heterogeneous groupings, where different ability levels are represented. Although that strategy often is recommended, research shows that high-achieving peers do not always benefit from being grouped with lower achievers (Hogan & Tudge,  1999 ), and the self-efficacy of lower achievers will not necessarily improve by watching higher achievers succeed (Schunk & Pajares,  2009 ). Whatever the means of grouping, teachers should ensure that each group can succeed with reasonable effort.

Groups also need guidance on what they are to accomplish—what is the expected product—as well as the expected mode of behavior. The task should be one that requires interdependence; no group member should be able to accomplish most of the entire task single-handedly. Ideally, the task also will allow for different approaches. For example, to address the topic of “Pirates in America,” a group of middle school students might give a presentation, use posters, conduct a skit, and involve class members in a treasure hunt.

Finally, it is important to ensure that each group member is accountable. If grades are given, it is necessary for group members to document what their overall contributions were to the group. A group in which only two of six members do most of the work but everyone receives an “A” is likely to breed resentment.

Two variations of cooperative learning are the jigsaw method and STAD (student-teams-achievement divisions). In the jigsaw method, teams work on material that is subdivided into parts. After each team studies the material, each team member takes responsibility for one part. The team members from each group meet together to discuss their part, after which they return to their teams to help other team members learn more about their part (Slavin,  1994 ). This jigsaw method combines many desirable features of cooperative learning, including group work, individual responsibility, and clear goals.

STAD groups study material after it has been presented by the teacher (Slavin,  1994 ). Group members practice and study together but are tested individually. Each member’s score contributes to the overall group score; but, because scores are based on improvement, each group member is motivated to improve—that is, individual improvements raise the overall group score. Although STAD is a form of cooperative learning, it seems best suited for material with well-defined objectives or problems with clear answers—for example, mathematical computations and social studies facts. Given its emphasis on improvement, STAD will not work as well where conceptual understanding is involved because student gains may not occur quickly.

CONSTRUCTIVIST LEARNING ENVIRONMENTS

Learning environments created to reflect constructivist principles look quite different from traditional classrooms (Brooks & Brooks,  1999 ). Learning in a constructivist setting is not allowing students to do whatever they want; rather, constructivist environments should create rich experiences that encourage learning. This section describes key features of constructivist learning environments including reflective teaching.

Key Features

Constructivist classrooms have several distinctive features that differ from those of traditional classrooms (Brooks & Brooks,  1999 ). In traditional classes, basic skills are emphasized. The curriculum is presented in small parts (e.g., units, lessons). Teachers disseminate information to students didactically and seek answers to questions. Assessment of student learning is distinct from teaching and usually done through testing. Students often work alone.

In constructivist classrooms, the curriculum focuses on big concepts. Activities typically involve primary sources of data and manipulative materials. Teachers interact with students by seeking their questions and points of view. Assessment is authentic; it is interwoven with teaching and includes teacher observations and student portfolios. Students often work in groups. The key is to structure the learning environment such that students can effectively construct new knowledge and skills (Schuh,  2003 ).

Some guiding principles of constructivist learning environments are shown in  Table 8.6  (Brooks & Brooks,  1999 ). One principle is that teachers should pose problems of emerging relevance to students, where relevance is preexisting or emerges through teacher mediation. Thus, a teacher might structure a lesson around questions that challenge students’ preconceptions. This takes time, which means that other critical content may not be covered. Relevance is not established by threatening to test students, but rather by stimulating their interest and helping them discover how the problem affects their lives.

A second principle is that learning should be structured around primary concepts. This means that teachers design activities around conceptual clusters of questions and problems so that ideas are presented holistically rather than in isolation (Brooks & Brooks,  1999 ). Being able to see the whole helps to understand the parts.

Holistic teaching does not require sacrificing content, but it does involve structuring content differently. A piecemeal approach to teaching history is to present information chronologically as a series of events. In contrast, a holistic method involves presenting themes that recur in history (e.g., economic hardships, disputes over territory) and structuring content so that students can discover these themes in different eras. Students then can see that although environmental features change over time (e.g., armies → air forces; farming → manufacturing), the themes remain the same.

Table 8.6 Guiding principles of constructivist learning environments.

Holistic teaching also can be done across subjects. In the middle school curriculum, for example, the theme of “courage” can be explored in social studies (e.g., courage of people to stand up and act based on their beliefs when these conflict with governments), language arts (e.g., characters in literature who display courage), and science (e.g., courage of scientists who dispute prevailing theories). An integrated curriculum in which teachers plan units together reflects this holism.

Third, it is important to seek and value students’ points of view. Understanding students’ perspectives is essential for planning activities that are challenging and interesting. This requires that teachers ask questions, stimulate discussions, and listen to what students say. Teachers who make little effort to understand what students think fail to capitalize on the role of their experiences in learning. This is not to suggest that teachers should analyze every student utterance; that is not necessary, nor is there time to do it. Rather, teachers should try to learn students’ conceptions of a topic.

With the current emphasis on achievement test scores, it is easy to focus only on students’ correct answers. Constructivist education, however, requires that—where feasible—we go beyond the answer and learn how the students arrived at that answer. Teachers do this by asking students to elaborate on their answers; for example, “How did you arrive at that answer?” or “Why do you think that?” It is possible for a student to arrive at a correct answer through faulty reasoning and, conversely, to answer incorrectly but engage in sound thinking. Students’ perspectives on a situation or theories about a phenomenon help teachers in curriculum planning.

Fourth, we should adapt curriculum to address students’ suppositions. This means that curricular demands on students should align with the beliefs they bring to the classroom. When there is a gross mismatch, lessons will lack meaning for students. But alignment need not be perfect. Demands that are slightly above students’ present capabilities (i.e., within the zone of proximal development) produce challenge and learning.

When students’ suppositions are incorrect, the typical response is to inform them of such. Instead, constructivist teaching challenges students to discover the information. Recall the opening vignette describing the mystery substance experiment. Students were baffled by the substance, which seemed at the same time both a liquid and solid. The teacher did not give them answers but rather challenged them to think about the substance and construct their understanding of it. By the end of the vignette, students still are not clear what the substance is, which suggests that more experimentation and discussion will follow.

Finally, constructivist education requires that we assess student learning in the context of teaching. This point runs counter to the typical classroom situation where most learning assessments are disconnected from teaching—for example, end-of-grade tests, end-of-unit exams, pop quizzes. Although the content of these assessments may align well with learning objectives addressed during instruction, the assessment occasions are separate from teaching.

In a constructivist environment, assessment occurs continuously during teaching and is an assessment of both students and teacher. In the opening vignette, the teacher assesses students’ thinking throughout the experiment, as well as her own success in designing an activity and guiding the students to construct their understandings.

Of course, assessment methods must reflect the type of learning ( Chapter 1 ). Constructivist environments are best designed for meaningful, deep-structure learning, not for superficial understanding. True-false and multiple-choice tests may be inappropriate to assess learning outcomes. Authentic forms of assessment may require students to write reflective pieces, discussing what they learned and why this knowledge is useful in the world, or to demonstrate and apply skills they have acquired.

Constructivist assessment is less concerned about right and wrong answers than about next steps after students answer. This type of authentic assessment guides instructional decisions, but it is difficult because it forces teachers to design activities that elicit student feedback and then alter instruction as needed. It is much easier to design and score a multiple-choice test, but encouraging teachers to teach constructively and then assess separately in a traditional manner sends a mixed message. Given the present emphasis on accountability, we may never completely move to authentic assessment; but encouraging it facilitates curricular planning and provides for more interesting lessons than drilling students to pass a test.

APA Learner-Centered Principles

The American Psychological Association formulated a set of learner-centered psychological principles (American Psychological Association Work Group of the Board of Educational Affairs,  1997 ;  Table 8.7 ) that reflect a constructivist learning approach. They were developed as guidelines for school design and reform.

The principles are grouped into four major categories: cognitive and metacognitive factors, motivational and affective factors, developmental and social factors, and individual differences. Cognitive and metacognitive factors involve the nature of the learning process, learning goals, construction of knowledge, strategic thinking, thinking about thinking, and the content of learning. Motivational and affective factors reflect motivational and emotional influences on learning, the intrinsic motivation to learn, and the effects of motivation on effort. Developmental and social factors include developmental and social influences on learning. Individual differences comprise individual difference variables, learning and diversity, and standards and assessment. These principles are reflected in current work on standards reform to address 21st-century skills.

Application 8.7  illustrates ways to apply these principles in learning environments. In considering their application, teachers should keep in mind the purpose of the instruction and the uses to which it will be put. Teacher-centered instruction often is the appropriate means of instruction and the most efficient. But when deeper student understanding is desired—along with greater student activity—the principles offer sound guidelines.

Reflective Teaching

Reflective teaching  is based on thoughtful decision making that takes into account knowledge about students, the context, psychological processes, learning and motivation, and knowledge about oneself. Although reflective teaching is not part of a constructivist perspective on learning, its premises are based on the assumptions of constructivism (Armstrong & Savage,  2002 ).

Components.

Reflective teaching stands in stark contrast to traditional teaching in which a teacher prepares a lesson, presents it to a class, gives students assignments and feedback, and evaluates their learning. Reflective teaching assumes that teaching cannot be reduced to one method to use with all students. Each teacher brings a unique set of experiences to teaching. How teachers interpret situations will differ depending on their experiences and perceptions. Professional development requires that teachers reflect on their beliefs and theories about students, content, context, and learning and check the validity of these beliefs and theories against reality.

Table 8.7 APA learner-centered principles.

Source: From “Learner-Centered Psychological Principles: A Framework for School Reform and Redesign.” Copyright ©1997 by the American Psychological Association. Reproduced with permission. No further reproduction or distribution is permitted without written permission from the American Psychological Association. The full document may be viewed at  http://www.apa.org/ed/governance/ea/learner-centered.pdf . The “Learner-Centered Psychological Principles” is a historical document which was derived from a 1990 APA presidential task force and was revised in 1997.

APPLICATION 8.7 Learner-Centered Principles

Mr. Donavan applies the APA learner-centered principles in his economics classes. He knows that many students are not intrinsically motivated to learn economics, so he builds into the curriculum strategies to enhance interest. He makes use of videos, field trips, and role playing to link economics better with real-world experiences. Mr. Donavan also does not want students to simply memorize content but rather learn to think critically. He teaches them a strategy to analyze events that includes key questions such as, What preceded the event? How might it have turned out differently? and How did this event influence future developments? Because he likes to focus on themes (e.g., economic development & policy), he has students apply these themes throughout the school year to different events.

Dr. Raimond is familiar with the APA principles and incorporates them into his teaching of educational psychology. He knows that students must have a good understanding of developmental, social, and individual difference variables if they are to be successful teachers. For their field placements, he ensures that students work in a variety of settings. Thus, students are assigned at different times to classes with younger and older students. He also ensures that students have the opportunity to work in classes where there is diversity in ethnic and socioeconomic backgrounds of students and with teachers whose methods use social interactions (e.g., cooperative learning, tutoring). Dr. Raimond understands the importance of students’ reflections on their experiences. They write journals on the field placement experiences and share these in class. He helps students understand how to link these experiences to topics they study in the course (e.g., development, motivation, learning).

Henderson ( 1996 ) listed four components of reflective teaching that involve decision making ( Table 8.8 ). Teaching decisions must be sensitive to the context, which includes the school, content, students’ backgrounds, time of the year, educational expectations, and the like. Fluid planning means that instructional plans must be flexible and change as conditions warrant. When students do not understand a lesson, it makes little sense to reteach it in the same way. Rather, the plan must be modified to aid student understanding.

Henderson’s model puts emphasis on teachers’ personal knowledge. They should be aware of why they do what they do and be keen observers of situations. They must reflect on and process a wide variety of information about situations. Their decisions are strengthened by professional development. Teachers must have a strong knowledge base from which to draw in order to engage in flexible planning and tailor lessons to student and contextual differences.

Reflective teachers are active persons who seek solutions to problems rather than wait for others to tell them what to do. They persist until they find the best solution rather than settle for one that is less than satisfactory. They are ethical and put students’ needs above their own; they ask what is best for students rather than what is best for them. Reflective teachers also thoughtfully consider evidence by mentally reviewing classroom events and revising their practices to better serve students’ needs. In summary, reflective teachers (Armstrong & Savage,  2002 ):

· ■ Use context considerations

· ■ Use personal knowledge

· ■ Use professional knowledge

· ■ Make fluid plans

· ■ Commit to formal and informal professional growth opportunities

We can see assumptions of constructivism that underlie these points. Constructivism places heavy emphasis on the context of learning because learning is situated. People construct knowledge about themselves (e.g., their capabilities, interests, attitudes) and about their profession from their experiences. Teaching is not a lockstep function that proceeds immutably once a lesson is designed. And finally, there is no “graduation” from teaching. Conditions always are changing, and teachers must stay at the forefront in terms of content, psychological knowledge of learning and motivation, and student individual differences.

Becoming a Reflective Teacher.

Being a reflective teacher is a skill, and like other skills it requires instruction and practice. The following suggestions are useful in developing this skill.

Being a reflective teacher requires good personal knowledge. Teachers have beliefs about their teaching competencies including subject knowledge, pedagogical knowledge, and student capabilities. To develop personal knowledge, teachers reflect on and assess these beliefs. Self-questioning is helpful. For example, teachers might ask themselves: “What do I know about the subjects I teach?” “How confident am I that I can teach these subjects so that students can acquire skills?” “How confident am I that I can establish an effective classroom climate that facilitates learning?” “What do I believe about how students can learn?” “Do I hold biases (e.g., that students from some ethnic or socioeconomic backgrounds cannot learn as well as other students)?”

Personal knowledge is important because it forms the basis from which to seek improvement. For example, teachers who feel they are not well skilled in using technology to teach social studies can seek professional development to aid them. If they find that they have biases, they can employ strategies so that their beliefs do not cause negative effects. Thus, if they believe that some students cannot learn as well as others, they can seek ways to help the former students learn better.

Being a reflective teacher also requires professional knowledge. Effective teachers are well skilled in their disciplines, understand classroom management techniques, and have knowledge about human development. Teachers who reflect on their professional knowledge and recognize deficiencies can correct them, such as by taking university courses or participating in staff development sessions on those topics.

Like other professionals, teachers must keep abreast of current developments in their fields. They can do this by belonging to professional organizations, attending conferences, subscribing to journals and periodicals, and discussing issues with colleagues.

Third, reflective teaching means planning and assessing. When reflective teachers plan, they do so with the goal of reaching all students. Many good ideas for lesson plans can be garnered from colleagues and practitioner journals. When students have difficulty grasping content presented in a certain way, reflective teachers consider other methods for attaining the same objective.

Assessment works together with planning. Reflective teachers ask how they will assess students’ learning outcomes. To gain knowledge of assessment methods, teachers may need to take courses or participate in staff development. The authentic methods that are in vogue now offer many possibilities for assessing outcomes, but teachers may need to consult with assessment experts and receive training on their use.

INSTRUCTIONAL APPLICATIONS

The educational literature is replete with examples of instructional applications that reflect constructivist principles. Some are summarized in this section.

The task facing teachers who attempt to implement constructivist principles can be challenging. Many are unprepared to teach in a constructivist fashion (Elkind,  2004 ), especially if their preparation programs have not stressed it. There also are factors associated with schools and school systems that work against constructivism (Windschitl,  2002 ). For example, school administrators and teachers are held accountable for students’ scores on standardized tests. These tests typically emphasize basic skills and downgrade the importance of deeper conceptual understanding. School cultures also may work against constructivism, especially if teachers have been teaching in the same fashion for many years and have standard curricula and lessons. Parents, too, may not be fully supportive of teachers using less direction in the classroom in favor of time for students to construct their understandings.

Despite these potential problems, there are many ways that teachers can incorporate constructivist teaching into their instruction and especially for topics that lend themselves well to it (e.g., discussion issues where there is no clearly correct answer). Three applications discussed here are discovery learning, inquiry teaching, and discussions and debates.

Discovery Learning

The Process of Discovery.

Discovery learning  refers to obtaining knowledge for oneself (Bruner,  1961 ). Discovery involves constructing and testing hypotheses rather than simply reading or listening to teacher presentations. Discovery is a type of  inductive reasoning , because students move from studying specific examples to formulating general rules, concepts, and principles. Discovery learning also is referred to as problem-based, inquiry, experiential, and constructivist learning (Kirschner et al.,  2006 ).

Discovery is a form of problem solving (Klahr & Simon,  1999 ;  Chapter 7 ); it is not simply letting students do what they want. Although discovery is a minimally guided instructional approach, it involves direction; teachers arrange activities in which students search, manipulate, explore, and investigate. The opening scenario represents a discovery situation. Students learn new knowledge relevant to the domain and such general problem-solving skills as formulating rules, testing hypotheses, and gathering information (Bruner,  1961 ).

Although some discoveries may be accidents that happen to lucky people, in fact most are planned and predictable. Consider how Pasteur developed the cholera vaccine (Root-Bernstein,  1988 ). Pasteur went on vacation during the summer of 1879. He had been conducting research on chicken cholera and left out germ cultures when he departed for 2 months.

·  Upon his return, he found that the cultures, though still active, had become avirulent; they no longer could sicken a chicken. So he developed a new set of cultures from a natural outbreak of the disease and resumed his work. Yet he found … that the hens he had exposed to the weakened germ culture still failed to develop cholera. Only then did it dawn on Pasteur that he had inadvertently immunized them. (p. 26)

This exemplifies most discoveries, which are not flukes but rather a natural (albeit possibly unforeseen) consequence of systematic inquiry by the discoverer. Discoverers cultivate their discoveries by expecting the unexpected. Pasteur did not leave the germ cultures unattended but rather in the care of his collaborator, Roux. When Pasteur returned from vacation, he inoculated chickens with the germs, and they did not become sick.

·  For some time, the strains that failed to kill chickens were also too weak to immunize them. But by March of 1880, Pasteur had developed two cultures with the properties of vaccines. The trick … was to use a mildly acidic medium … and to leave the germ culture sitting in it … He produced an attenuated organism capable of inducing an immune response in chickens. The discovery … was not an accident at all; Pasteur had posed a question—Is it possible to immunize an animal with a weakened infectious agent?—and then systematically searched for the answer. (Root-Bernstein,  1988 , p. 29)

To discover knowledge, students require background knowledge ( Chapter 5 ). Once students possess prerequisite knowledge, careful structuring of material allows them to discover important principles.

Teaching for Discovery.

Teaching for discovery requires presenting questions, problems, or puzzling situations to resolve and encouraging learners to make intuitive guesses when they are uncertain. In leading a class discussion, teachers could ask questions that have no readily available answers and tell students that their answers will not be graded, which forces students to construct their understandings. Discoveries are not limited to activities within school. During a unit on ecology, students could discover why animals of a given species live in certain areas and not in others. Students might seek answers in classroom workstations, in the school media center, and on or off the school grounds. Teachers provide structure by posing questions and giving suggestions on how to search for answers. Greater teacher structure is beneficial when students are not familiar with the discovery procedure or require extensive background knowledge. Other examples are given in  Application 8.8 .

APPLICATION 8.8 Discovery Learning

Learning becomes more meaningful when students explore their learning environments rather than listen passively to teachers. An elementary teacher used guided discovery to help her children learn animal groups (e.g., mammals, birds, reptiles). Rather than providing students with the basic animal groups and examples for each, she asked students to provide the names of types of animals. Then she helped students classify the animals by examining their similarities and differences. Category labels were assigned once classifications are made. This approach is guided to ensure that classifications are proper, but students are active contributors as they discover the similarities and differences among animals.

A high school chemistry teacher might use “mystery” liquids and have students discover the elements in each. The students could proceed through a series of tests designed to determine if certain substances are present in a sample. By using the experimental process, students learn about the reactions of substances to certain chemicals and also how to determine the contents of their substances.

A university professor uses other problem-based learning activities in his class. He creates different classroom scenarios that describe situations involving student learning and behaviors, as well as teacher actions. He divides his students into small groups and asks them to work through each scenario and discover which learning principles best describe the situations presented.

Discovery is not appropriate for all types of learning. Discovery can impede learning when students have no prior experience with the material or background information (Tuovinen & Sweller,  1999 ). Teaching for discovery learning may not be appropriate with well-structured content that is easily presented. Students could discover which historical events occurred in which years, but this is trivial learning. If they arrived at the wrong answers, time would be wasted in reteaching the content. Discovery seems more appropriate when the learning process is important, such as with problem-solving activities that motivate students to learn and acquire the requisite skills. However, establishing discovery situations (e.g., growing plants) often takes time, and experiments might not work.

As a type of minimally guided instruction, discovery learning can enhance students’ problem solving and self-regulated learning (Hmelo-Silver,  2004 ), but it has drawn criticism. Mayer ( 2004 ) reviewed research from the 1950s to the 1980s that compared pure discovery learning (i.e., unguided, problem-based learning) with guided instruction. The research showed that guided instruction produced superior learning. In a subsequent review, Alfieri, Brooks, Aldrich, and Tenenbaum ( 2011 ) found that explicit instruction promoted learning outcomes better than unassisted discovery.

Notice that these criticisms pertain to minimally guided instruction. Alfieri et al. ( 2011 ) also found in their review that assisted (guided) discovery was generally more effective than other forms of instruction. In guided discovery, teachers arrange the situation such that learners are not left to their own devices but rather receive support. Guided discovery also makes good use of the social environment—a key feature of constructivism. Supports (scaffolding) for learning can be minimized when learners have developed some skills and therefore can guide themselves. In deciding whether to use discovery, teachers should take into account the learning objectives (e.g., acquire knowledge or learn problem-solving skills), time available, and cognitive capacities of the students.

Inquiry Teaching

Inquiry teaching  is a form of discovery learning, although it can be structured to have greater teacher direction. In an inquiry model based on the Socratic teaching method (Collins,  1977 ; Collins & Stevens,  1983 ), the goals are to have students reason, derive general principles, and apply them to new situations. Appropriate learning outcomes include formulating and testing hypotheses, differentiating necessary from sufficient conditions, making predictions, and determining when making predictions requires more information.

In implementing the model, the teacher repeatedly questions the student. Questions are guided by rules such as “Ask about a known case,” “Pick a counterexample for an insufficient factor,” “Pose a misleading question,” and “Question a prediction made without enough information” (Collins,  1977 ). Rule-generated questions help students formulate general principles and apply them to specific problems.

The following is a sample dialogue between teacher (T) and student (S) on the topic of population density (Collins,  1977 ):

Although this instructional approach was designed for one-to-one tutoring, with some modifications it seems appropriate with small groups of students. One issue is that persons who serve as tutors require extensive training to pose appropriate questions in response to a student’s level of thinking. Also, good content-area knowledge is a prerequisite for problem-solving skills. Students who lack a decent understanding of basic knowledge are not likely to function well under an inquiry system designed to teach reasoning and application of principles. Other student characteristics (e.g., age, abilities) also may predict success under this model. As with other constructivist methods, teachers must consider the student outcomes and the likelihood that students can successfully engage in the inquiry process.

Discussions and Debates

Class discussions are useful when the objective is to acquire greater conceptual understanding or multiple sides of a topic. The topic being discussed is one for which there is no clear right answer but rather involves a complex or controversial issue. Students enter the discussion with some knowledge of the topic and are expected to gain understanding as a result of the discussion.

Discussions lend themselves to various disciplines, such as history, literature, science, and economics. Regardless of the topic, it is critical that a class atmosphere be created that is conducive to free discussion. Students likely will have to be given rules for the discussion (e.g., do not interrupt someone who is speaking, keep arguments to the topic being discussed, do not personally attack other students). If the teacher is the facilitator of the discussion, then he or she must support multiple viewpoints, encourage students to share, and remind students of the rules when they are violated. Teachers also can ask students to elaborate on their opinions (e.g., “Tell us why you think that.”).

When class size is large, small-group discussions may be preferable to whole-class ones. Students reluctant to speak in a large group may feel less inhibited in a smaller one. Teachers can train students to be facilitators of small-group discussions.

A variation of the discussion is the debate, in which students selectively argue sides of an issue. This requires preparation by the groups and, likely, some practice if they will be giving short presentations on their sides. Teachers enforce rules of the debate and ensure that all team members participate. A larger discussion with the class can follow, which allows for points to be reinforced or new points brought up.

SUMMARY

Constructivism is an epistemology, or philosophical explanation about the nature of learning. Constructivist theorists reject the idea that scientific truths exist and await discovery and verification. Knowledge is not imposed from outside people but rather formed inside them. Constructivist theories vary from those that postulate complete self-construction, through those that hypothesize socially mediated constructions, to those that argue that constructions match reality. Constructivism requires that we structure teaching and learning experiences to challenge students’ thinking so that they will be able to construct new knowledge. A core premise is that cognitive processes are situated (located) within physical and social contexts. The concept of situated cognition highlights these relations between persons and situations.

Piaget’s theory is constructivist and postulates that children pass through a series of qualitatively different stages: sensorimotor, preoperational, concrete operational, and formal operational. The chief developmental mechanism is equilibration, which helps to resolve cognitive conflicts by changing the nature of reality to fit existing structures (assimilation) or changing structures to incorporate reality (accommodation).

Bruner’s theory of cognitive growth discusses the ways that learners represent knowledge: enactively, iconically, and symbolically. He advocated the spiral curriculum, in which subject matter is periodically revisited with increasing cognitive development and student understanding.

Vygotsky’s sociocultural theory emphasizes the social environment as a facilitator of development and learning. The social environment influences cognition through its tools—cultural objects, language, symbols, and social institutions. Cognitive change results from using these tools in social interactions and from internalizing and transforming these interactions. A key concept is the zone of proximal development, which represents the amount of learning possible by a student given proper instructional conditions. It is difficult to evaluate the contributions of Vygotsky’s theory to learning because most research is recent and many educational applications that fit with the theory are not part of it. Applications that reflect Vygotsky’s ideas are instructional scaffolding, reciprocal teaching, peer collaboration, and apprenticeships.

Private speech has a self-regulatory function, but is not socially communicative. Vygotsky believed that private speech develops thought by organizing behavior. Children employ private speech to understand situations and surmount difficulties. Private speech becomes covert with development, although overt verbalization can occur at any age. Verbalization can promote student achievement if it is relevant to the task and does not interfere with performance.

Vygotsky’s theory contends that learning is a socially mediated process. Children learn many concepts during social interactions with others. Structuring learning environments to promote these interactions facilitates learning. Peer-assisted learning, which is a type of socially mediated learning, refers to instructional approaches in which peers serve as active agents in the learning.

The goal of constructivist learning environments is to provide rich experiences that encourage students to learn. Constructivist classrooms teach big concepts using much student activity, social interaction, and authentic assessments. Students’ ideas are avidly sought, and, compared with traditional classes, there is less emphasis on superficial learning and more emphasis on deeper understanding. The APA learner-centered principles, which address various factors (cognitive, metacognitive, motivational, affective, developmental, social, and individual differences), reflect a constructivist learning approach. Reflective teaching is thoughtful decision making that considers such factors as students, contexts, psychological processes, learning, motivation, and self-knowledge. Becoming a reflective teacher requires developing personal and professional knowledge, planning strategies, and assessment skills.

Some instructional methods that fit well with constructivism are discovery learning, inquiry teaching, and discussions and debates. Discovery learning allows students to obtain knowledge for themselves through problem solving. Discovery requires that teachers arrange activities such that students can form and test hypotheses. It is not simply letting students do what they want. Inquiry teaching is a form of discovery learning that may follow Socratic principles with much teacher questioning of students. Discussions and debates are useful when the objective is to acquire greater conceptual understanding or multiple viewpoints of a topic. A summary of learning issues relevant to constructivism appears in  Table 8.9 .

Table 8.9 Summary of learning issues.