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The Psychology of Academic Achievement Philip H. Winne and John C. Nesbit Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; email: [email protected], [email protected]

Annu. Rev. Psychol. 2010. 61:653–78

First published online as a Review in Advance on October 19, 2009

The Annual Review of Psychology is online at psych.annualreviews.org

This article’s doi: 10.1146/annurev.psych.093008.100348

Copyright c© 2010 by Annual Reviews. All rights reserved

0066-4308/10/0110-0653$20.00

Key Words

school learning, educational psychology, motivation, metacognition, experimental methodology, self-regulated learning

Abstract Educational psychology has generated a prolific array of findings about factors that influence and correlate with academic achievement. We re- view select findings from this voluminous literature and identify two do- mains of psychology: heuristics that describe generic relations between instructional designs and learning, which we call the psychology of “the way things are,” and findings about metacognition and self-regulated learning that demonstrate learners selectively apply and change their use of those heuristics, which we call the psychology of “the way learn- ers make things.” Distinguishing these domains highlights a need to marry two approaches to research methodology: the classical approach, which we describe as snapshot, bookend, between-group experimen- tation; and a microgenetic approach that traces proximal cause-effect bonds over time to validate theoretical accounts of how learning gen- erates achievements. We argue for fusing these methods to advance a validated psychology of academic achievement.

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Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . 654 COGNITIVE FACTORS . . . . . . . . . . . . 655

The Example of Cognitive Load . . . . 655 METACOGNITIVE FACTORS . . . . . . 657 MOTIVATIONAL FACTORS . . . . . . . . 659

Achievement Goals . . . . . . . . . . . . . . . . 659 Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . 660 Epistemic Beliefs . . . . . . . . . . . . . . . . . . . 661

CONTEXT FACTORS . . . . . . . . . . . . . . 661 Peer-Supported Learning . . . . . . . . . . 661 Classrooms and Class Size . . . . . . . . . . 663 Homework . . . . . . . . . . . . . . . . . . . . . . . . 664 Socioeconomic Status . . . . . . . . . . . . . . 666

PERSISTENT DEBATES . . . . . . . . . . . . 666 Learning and Cognitive Styles . . . . . . 666 Discovery Learning . . . . . . . . . . . . . . . . 667

METHODOLOGICAL ISSUES IN MODELING A PSYCHOLOGY OF ACADEMIC ACHIEVEMENT . . . 669 Paradigmatic Issues . . . . . . . . . . . . . . . . 669 A Revised Paradigm . . . . . . . . . . . . . . . . 671

SHAPES FOR FUTURE RESEARCH . . . . . . . . . . . . . . . . . . . . . . 671

INTRODUCTION

“Extensive” significantly understates the scope of research relevant to a psychology of academic achievement. Not having examined all relevant books, chapters, proceedings, and articles—a task we estimate might require three decades of full-time work—we nonetheless posit it is possible to develop a unified account of why, how, and under what conditions learners suc- ceed or fail in school. That account could lead to powerful theories about improving educational practices. Advancing toward such a model is our aim here although, necessarily, much has been omitted from our review. Like all models, our model will have limitations.

The model we sketch acknowledges two cat- egories of psychological phenomena. The first concerns a psychology of “the way things are.” By this we mean psychological phenomena that,

in principle, are universal among learners and across subject areas and are not likely under learners’ control. One example is that cognition can simultaneously manage only a limited num- ber of tasks or chunks of information. Another is that learners express biases that can be shaped by information in their environment. This is the framing effect. A third is that information studied and then immediately restudied will be recalled less completely and less accurately than if restudying is delayed.

The second category concerns a psychology of “the way learners make things.” In this cat- egory we consider learners as agents. Agents choose among tasks and among psychological tools for working on tasks. An example is decid- ing whether to prepare for an exam by massed or spaced review. Another example is deciding whether and how long to try retrieving infor- mation when it can’t be found but there is a feeling of knowing it. If learners have knowl- edge of several mnemonic techniques for re- calling information, they can choose among those mnemonics. If a first choice fails but strengthens the feeling of knowing, learners can metacognitively monitor what they did to make an informed choice about the next mnemonic technique to try. They have the option to in- terpret success and failure as due to effort or ability. When these choices are made and acted on, new information is created and feeds for- ward. In this way, learners shape their learning environment.

Is it important to distinguish between psy- chologies of the way things are and the way learners make things? In his recent review of research on memory, Roediger (2008, p. 247) wrote: “The aim of this review has been to re- mind us of the quest for laws and the difficulty in achieving them. . . . The most fundamental principle of learning and memory, perhaps its only sort of general law, is that in making any generalization about memory one must add that ‘it depends.’” We suggest Roediger’s lament may derive from failing to incorporate our dis- tinction. While one significant source of vari- ance in the psychology of academic achieve- ment is due to the way things are, a second

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significant source of variance originates in the psychology of the way learners make things. We argue that a psychology of academic achieve- ment must account for how each psychology separately and jointly affects achievement.

Our account of the psychology of academic achievement also borrows a view presented by Borsboom et al. (2003). In brief, they argue and we agree that both kinds of psychology have been hampered, even misled, by failing to address proximal psychological processes. We consider questions about psychological pro- cesses that are shaped and constrained by how things are, and about processes that provide tools with which learners make things. In our account, we portray academic achievement as the result of self-regulated learning and argue that improving research entails rethinking con- structs and the paradigm that guides experi- mental research.

COGNITIVE FACTORS

Since the publication of Thorndike’s (1903) classic book Educational Psychology, the field has generated thousands of studies. Most investi- gated how environmental factors can be de- signed and how conditions within learners can be arranged to promote learning facts, princi- ples, skills, and schemas. Recently, a consortium of approximately 35 eminent researchers (see http://psyc.memphis.edu/learning/index. shtml) summarized from this voluminous library 25 empirically grounded heuristics for instructional designs (see Table 1).

Intending no slight to the range of work con- tributing to each heuristic, we choose cognitive load theory to epitomize the category of a psy- chology describing “the way things are.”

The Example of Cognitive Load

The construct of cognitive load has proven a powerful explanatory device for spanning the oft-cited gap between a science of learning and the arts of teaching and instructional de- sign. Sweller (1988) developed cognitive load theory from models of working memory (e.g.,

Baddeley & Hitch 1974) that emphasized the limited capacity of working memory as a fun- damental resource bottleneck in cognition. Vis-à-vis instruction, cognitive load is the total processing required by a learning activity. It has three components. First, intrinsic load is due to the inherent difficulty of an instructional task. It is indexed by the number of active interact- ing schemas needed to perform the task. Intrin- sic load cannot be directly reduced by manip- ulating instructional factors. However, as the learner forms schemas and gains proficiency, intrinsic load decreases. Second, germane load arises from the cognitive processing that forms those schemas and boosts proficiency. Third, extrinsic cognitive load is any unnecessary pro- cessing. This load can be eliminated by manip- ulating instructional factors.

The three forms of cognitive load are addi- tive; their sum cannot exceed working memory’s limited capacity (Paas et al. 2003a). Intrinsic processing receives priority access to working memory. Remaining capacity is shared between germane and extrinsic processing. When total load is less than available capacity, an instruc- tional designer, teacher, or learner can deliber- ately increase germane load to increase learning efficiency. Changing instructional factors may reduce extrinsic load. If working memory ca- pacity is fully loaded, this can free resources for germane processing and ultimately produce more efficient learning. Total cognitive load has been measured by real-time recordings of per- formance and psychophysiological indices. It is most commonly gauged by self-report ratings collected after the task (Paas et al. 2003b).

Cognitive load is now liberally cited as an explanatory construct in research ranging over chemistry problem solving (Ngu et al. 2009), moral reasoning (Murphy et al. 2009), driver performance (Reyes & Lee 2008), and even motherhood (Purhonen et al. 2008). When cited by researchers outside the learning sci- ences, the tripartite nature of cognitive load is typically disregarded.

Reducing extraneous cognitive load links to several heuristics in Table 1. It is the primary theoretical grounding for improving learning

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Table 1 Twenty-five heuristics for promoting learninga

Contiguity effects Ideas that need to be associated should be presented contiguously in space and time. Perceptual-motor grounding Concepts benefit from being grounded in perceptual motor experiences, particularly at early

stages of learning. Dual code and multimedia effects Materials presented in verbal, visual, and multimedia form richer representations than a single

medium. Testing effect Testing enhances learning, particularly when the tests are aligned with important content. Spacing effect Spaced schedules of studying and testing produce better long-term retention than a single

study session or test. Exam expectations Students benefit more from repeated testing when they expect a final exam. Generation effect Learning is enhanced when learners produce answers compared to having them recognize

answers. Organization effects Outlining, integrating, and synthesizing information produces better learning than rereading

materials or other more passive strategies. Coherence effect Materials and multimedia should explicitly link related ideas and minimize distracting

irrelevant material. Stories and example cases Stories and example cases tend to be remembered better than didactic facts and abstract

principles. Multiple examples An understanding of an abstract concept improves with multiple and varied examples. Feedback effects Students benefit from feedback on their performance in a learning task, but the timing of the

feedback depends on the task. Negative suggestion effects Learning wrong information can be reduced when feedback is immediate. Desirable difficulties Challenges make learning and retrieval effortful and thereby have positive effects on long-term

retention. Manageable cognitive load The information presented to the learner should not overload working memory. Segmentation principle A complex lesson should be broken down into manageable subparts. Explanation effects Students benefit more from constructing deep coherent explanations (mental models) of the

material than memorizing shallow isolated facts. Deep questions Students benefit more from asking and answering deep questions that elicit explanations (e.g.,

why, why not, how, what-if ) than shallow questions (e.g., who, what, when, where). Cognitive disequilibrium Deep reasoning and learning is stimulated by problems that create cognitive disequilibrium,

such as obstacles to goals, contradictions, conflict, and anomalies. Cognitive flexibility Cognitive flexibility improves with multiple viewpoints that link facts, skills, procedures, and

deep conceptual principles. Goldilocks principle Assignments should not be too hard or too easy, but at the right level of difficulty for the

student’s level of skill or prior knowledge. Imperfect metacognition Students rarely have an accurate knowledge of their cognition, so their ability to calibrate their

comprehension, learning, and memory should not be trusted. Discovery learning Most students have trouble discovering important principles on their own, without careful

guidance, scaffolding, or materials with well-crafted affordances. Self-regulated learning Most students need training in how to self-regulate their learning and other cognitive

processes. Anchored learning Learning is deeper and students are more motivated when the materials and skills are anchored

in real-world problems that matter to the learner.

a Reproduced from http://psyc.memphis.edu/learning/whatweknow/index.shtml. An elaborated description of each principle plus citations identifying empirical support is available as 25 Learning Principles to Guide Pedagogy and the Design of Learning Environments. Retrieved Jan. 2, 2009 from http://psyc. memphis.edu/learning/whatweknow/25principles.doc.

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by eliminating unnecessary information (co- herence), cueing learners’ attention (signaling), colocating items to be mentally integrated (spa- tial contiguity), and synchronizing events to be mentally integrated (temporal contiguity) (Mayer 2005).

Laboratory tasks designed to elevate cogni- tive load are reported by learners to feel more difficult (Paas et al. 2003b). From this, we as- sume the state of working memory overload is consciously experienced. Thus, it is within the purview of metacognition. Students can avoid overload by segmenting complex tasks for sequential work or using external mnemonics such as notes or diagrams. The cost of adopting learning tactics is initially experienced as added difficulty. But this investment can pay off in the long run.

METACOGNITIVE FACTORS

Flavell (1971) is credited with motivating psy- chologists to research the “intelligent moni- toring and knowledge of storage and retrieval operations—a kind of metamemory, perhaps” (p. 277). He succeeded wildly. Since then, the broader topic of metacognition—cognition fo- cused on the nature of one’s thoughts and one’s mental actions, and exercising control over one’s cognitions—has generated a body of work that merits its own Handbook of Metacognition in Education (Hacker et al. 2009).

Metacognition is basically a two-step event with critical features. First, learners monitor features of a situation. They may monitor their knowledge, whether a peer or resource can pro- vide information, and possible consequences if they make a particular move in solving a problem. The metacognitive account of the situation is determined by what the learner perceives, which may differ from its actual qual- ities. Monitoring compares those perceived features to standards set by the learner. Often, these are linked to but not necessarily identical to standards indicated by a teacher, parent, or peer. Second, based on the profile of differences between the learner’s perception of the situa- tion and standards—which differences there are

and how large they are—the learner exercises control. The learner may choose to stay the prior course at a task’s midpoint, adapt slightly or significantly, or exit the task to pursue something else. Together, these steps set the stage for self-regulated learning, a potentially ubiquitous activity (Winne 1995).

Learners are considered agents. This means they choose whether and how to engage in tasks. But learners are not omnipotent. Nor are they insulated from their cerebral and the ex- ternal worlds. Agency is reciprocally governed: As learners change their local environment, the environment’s web of causal factors modulates affordances available to them (Martin 2004). For example, having monitored a problem’s statement and classified it as solvable, inher- ent spreading activation in memory may render information that the problem is difficult. This may arouse anxiety. Seeking information from a peer may return a reply that warrants a pos- itive attribution to effort. Or, it may generate a negative view that success can’t be achieved without help from others. Some information the environment provides (e.g., by spreading activation) is not controllable, whereas other information (e.g., the affect associated with a peer’s assessment) can be at least partially the learner’s choice.

Given this account, four metacognitive achievements can be identified: (a) alertness to occasions to monitor, (b) having and choos- ing useful standards for monitoring, (c) accu- racy in interpreting the profile generated by monitoring, and (d ) having and choosing use- ful tactics or strategies. After setting the stage to reach subject matter achievements by develop- ing these metacognitive skills, two further steps are required: (e) being motivated to act and ( f ) modifying the environment or locating oneself in an environment that affords the chosen ac- tion (Winne & Nesbit 2009).

Alertness to occasions appropriate to metacognitive monitoring has not been much researched beyond studies of readers’ capabili- ties to detect superficial (e.g., spelling) or mean- ingful errors in texts. In this limited domain, detecting errors is proportional to measures of

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prior achievement and inversely proportional to load on working memory (Oakhill et al. 2005, Walczyk & Raska 1992). The former suggests that standards used in monitoring derive from prior knowledge, similar to what learners use to construct a situation model for new infor- mation (Kintsch 1988). The latter reflects that working memory’s resources play a ubiquitous role in the economy of information processing.

Learners may struggle to assimilate use- ful standards and apply them in monitoring. Beyond simplistic misperceptions about what counts when assignments are graded, learners may focus on information at the wrong grain size. They may judge work at a global level when more-specific targets or items should be the standard (Dunlosky et al. 2005).

Research on learners’ accuracy of metacog- nitive monitoring has blossomed under the rubric of judgments of learning. It is rooted in the concept of feeling of knowing (Hart 1965), a belief that information is in memory although it cannot be retrieved. There are four main findings. First, learners are poor at monitoring learning and have a bias toward overconfidence (Maki 1998). Second, engaging with informa- tion in meaningful ways, such as generating a summary of a large amount of information, can improve accuracy (see Thomas & McDaniel 2007). Third, accuracy improves by delaying monitoring so that learners experience recall (or lack of it) rather than just scan residual informa- tion in working memory (Koriat 1993, Nelson & Dunlosky 1991, Thiede et al. 2005). Fourth, after experiencing difficulty in recall, judgments shift from being overconfident to the oppo- site, dubbed the “underconfidence with practice effect” (Koriat et al. 2002).

Relatively much more research is avail- able about tools learners have for exercising metacognitive control. These tools, commonly termed metacognitive skills or learning strate- gies, vary widely and are researched using two common experimental formats. The first trains learners to competence in a tactic and then compares pretraining performance to post- training performance. The second compares trained learners to a group not trained in the

tactic. Early studies investigated very specific learning tactics, such as whether young children could verbally mediate how they learned asso- ciations when rules governing associative pairs changed (Kendler et al. 1972). At the other end of this continuum, Dansereau and colleagues (see Dansereau 1985) trained undergraduates in a typology of strategies summarized by the acronym MURDER: set mood, understand requirements of a task, recall key features of task requirements, detail (elaborating) main ideas studied, expand information into orga- nized forms (e.g., an outline), and review. In a semester-long course, students showed statisti- cally detectable but modest benefits when using MURDER (Dansereau et al. 1979). Other research investigated various methods for engaging learners with information and pro- viding opportunities to monitor (see Thomas & McDaniel 2007), including deciding when to stop initial study and when to restudy (see Rohrer & Pashler 2007), self-questioning (Davey & McBride 1986), and summarizing information in keyword (Thiede et al. 2003) or prose form (Thiede & Anderson 2003).

Haller et al. (1988) meta-analyzed 20 stud- ies on the effects of metacognitive instruction on reading comprehension. The average ef- fect size was 0.72. Hattie and colleagues (1996) meta-analyzed 51 newer studies in reading and other subject areas. The average effect sizes due to training in cognitive or metacognitive skills were 0.57 on performance, 0.16 on study skills expertise, and 0.48 on positive affect. Because comparison groups typically represent “business as usual” conditions, two corollaries are warranted: Learners don’t naturally learn metacognitive skills to an optimum level, and schooling does not sufficiently remedy this dis- advantage. Findings show training has immedi- ate benefits, but they leave unanswered a critical question: Do positive effects of training persist and transfer?

Dignath et al. (2008) meta-analyzed re- search investigating whether primary school children could be trained to use theoreti- cally more effective forms of self-regulated learning than they had developed themselves

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and, if so, whether training benefited reading, writing, mathematics, science, other areas of academic performance, attributions, self- efficacy, and metacognitive strategies. Overall, various kinds of training in self-regulated learn- ing produced a weighted effect size of 0.69. But there were two notable issues. First, results were quite variable. Second, the research was overly dependent on self-reports about psychological events such as metacognition and uses of learn- ing tactics.

Metacognition is not “cold”—affect and motivationally “hot” variables interact, includ- ing attributions (Hacker et al. 2008), goal orientations (Vrugt & Oort 2008), epistemo- logical beliefs (Pieschl et al. 2008), and self- efficacy. The picture here is complex and incon- sistent, in part because learners’ self-reports of motivation may not correspond to choices they make to study (Zhou 2008). A broader model of metacognition is needed.

MOTIVATIONAL FACTORS

Motivation is conceptualized as a factor that influences learning. It also is an outcome of learning sought for its own sake. As an influ- ence, motivation divides into two broad cate- gories: factors that direct or limit choices for engagement—choosing to study history for in- terest but mathematics out of necessity, and factors that affect intensity of engagement— trying hard versus barely trying. As an outcome, motivations concern satisfaction or some other inherent value.

The vast span of theories and empiri- cal work on motivational factors and aca- demic achievement was surveyed, in part, by Covington (2000) and Meece et al. (2006). Both reviews emphasized research on motiva- tion arising from goal-orientation frameworks, so we briefly update that topic before turning to other issues.

Covington (2000) divided the field into two sectors grounded in Kelly’s (1955) distinction between (a) motives as drives, “an internal state, need or condition that impels individuals to- ward action” (p. 173) and (b) motives as goals,

where “actions are given meaning, direction, and purpose by the goals that individuals seek out, and. . . the quality and intensity of behav- ior will change as these goals change” (p. 174). As Covington noted, this distinction can be arbitrary because the same behavior can be conceived as reflecting both forms.

We scan three main areas of contempo- rary research, acknowledging that others are omitted. Our choices reflect a judgment about the intensity of recent work in educational psychology and fit our view of learners as self-regulating.

Achievement Goals

Achievement goals describe what learners ori- ent to when learning, particularly the instru- mental role of what is learned. The main re- search question has been whether achievement goals existing before learning is engaged corre- late with levels or types of learning. The reviews by Covington (2000) and Meece et al. (2006) provide ample evidence that different goals cor- relate variously with outcomes.

A more interesting issue for self-regulated learning is whether achievement goals shape or constrain activities learners choose as they strive for goals. According to this view, goals play the role of standards for metacogni- tively monitoring situations—a task or the classroom—to classify them in terms of options for behavior. For example, students holding mastery approach goals, defined as intentions to deeply and thoroughly comprehend a sub- ject, may judge that a situation affords oppor- tunity to substantially extend expertise. In con- trast, learners with performance approach goals may classify that same situation (as an observer determines sameness) as offering excellent chances to prove competence to others. Because of their differing classifications, these learners may exercise metacognitive control to choose very different tactics for learning (e.g., Dweck & Master 2008, Kolic-Vehovec et al. 2008, Miki & Yamauchi 2005, Pintrich & De Groot 1990).

This line of research faces several chal- lenges. First, learners are not unidimensional

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in their goal orientations (Pintrich 2000), so bindings between goal orientations and learn- ing events are correspondingly complicated. Second, self-reports have been almost the only basis for researchers to identify goal ori- entation(s) (cf. Zhou 2008). One-time self- reports about adopted goals have some inherent validity—learners’ declarations are what they are. But goals may be unstable, and the task’s context may differ from the survey’s context (Dowson et al. 2006). Like goal orientations, self-reports are almost the only data gathered to reflect tactics that learners use in learning. These self-reports also are contextually sensi- tive (Hadwin et al. 2001) and may not be trust- worthy accounts of tactics learners actually use during study ( Jamieson-Noel & Winne 2003, Winne & Jamieson-Noel 2002).

Together, these challenges weaken prior accounts about how goal orientations lead to choices of learning tactics that directly raise achievement. In addition to develop- ing performance-based measures, gaining ex- perimental control over goal orientation is a promising strategy for advancing research in this area (Gano-Overway 2008).

Interest

Interest predicts choices that learners make about where and how intensely to focus atten- tion; whether to engage in an activity; and the intensity of, concentration on, or persistence in that engagement. Interest also describes a psychological state of positive affect related to features a learner perceives about the environ- ment. Following a revival of research on interest and learning in the early 1990s (Renninger et al. 1992), two main forms of interest have been differentiated. Individual interest captures the predictive quality of interest, as in “I’m inter- ested in science.” Situational interest arises ei- ther from an opportunistic interaction between a person and features of the transient environ- ment or because a learner exercises volition to create a context that is interesting.

Krapp (2005) reviewed research supporting a model that interest arises because learners

experience feedback as they work. His model echoes Dewey’s (1913) notion that a fusion of productive cognition and positive affect abets interest. Specifically, when feedback about task engagement supports a view of oneself as com- petent, agentic, and accepted by others, the task and its method of engagement acquire a degree of interest. Future tasks can be moni- tored for similar qualities, and the learner ac- cordingly regulates future perceptions as well as engagement.

Research on interest documents that when a situation is monitored to match a priori in- terest, learners choose that situation, persist, and report positive affect as expected. As a con- sequence of persistence, learners usually learn more (Ainley et al. 2002). However, interest can debilitate when it leads learners to regulate learning by allocating more or more-intense cognitive processing to less-relevant but inter- esting content (Lehman et al. 2007, Senko & Miles 2008).

Interest dynamically interacts in complex ways with other variables that mediate the ef- fects of interest and interest itself. A tiny sam- ple of the roll call of these variables follows. Prior interest (Randler & Bogner 2007), prior knowledge, and the structure of knowledge in the domain (Lawless & Kulikowich 2006) all increase achievement and correlate with higher interest. Mastery goals and values attributed to tasks regarding their future utility and enjoy- ment (Hulleman et al. 2008) predict higher in- terest but not necessarily higher achievement. Self-concept of ability (Denissen et al. 2007) positively correlates with interest and medi- ates achievement. Need for cognition (Dai & Wang 2007) does the same. To this list we add self-monitoring and regulation, which we the- orize increase students’ sense of task-specific agency and consequently interest (Goddard & Sendi 2008). Given the centrality of teachers’ and parents’ concerns about students’ inter- ests in school topics and tasks, this tangle of findings begs for order. Some order might be achieved by applying Occam’s razor to coalesce an overabundance of currently differentiated variables.

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Epistemic Beliefs

Epistemic beliefs describe views a learner holds about features that distinguish information from knowledge, how knowledge originates, and whether and how knowledge changes. Two studies sparked an explosion of research in this area. The first was Perry’s (1970) longitudi- nal study of undergraduates’ developing views of these topics. The second was Schommer’s (1990) extension of Ryan’s (1984) study, show- ing that epistemic beliefs moderated compre- hension of text.

A general conclusion is that epistemic be- liefs predict interactions: When information is complex and probabilistic and its applica- tion in tasks cannot be definitively prescribed— when a task is ill-structured—learners who hold less well developed and less flexible epistemic beliefs recall, learn, argue, and solve prob- lems less well than do peers with better devel- oped snd more flexible epistemic beliefs (e.g., Mason & Scirica 2006, Stathopoulou & Vosniadou 2007). But when tasks and informa- tion are not ill structured, holding sophisticated epistemological beliefs can interfere with re- call and comprehension (Bräten et al. 2008). In short, match of aptitude to task matters.

Muis (2007) synthesized theory and research on epistemic beliefs and self-regulated learning. She offered four main conclusions. First, learn- ers observe features of tasks that reflect epis- temic qualities (Muis 2008). Second, they use these perceptions to set goals and frame plans for accomplishing work. Third, as work on a task proceeds, learners use epistemic standards to metacognitively monitor and regulate learn- ing processes (Dahl et al. 2005). Last, engag- ing in successful self-regulated learning can al- ter epistemic beliefs, specifically, toward a more constructivist stance (Verschaffel et al. 1999).

CONTEXT FACTORS

Peer-Supported Learning

Peer-supported learning encompasses collabo- rative, cooperative, and small-group arrange- ments in dyads or groups of up to about six

members. It is theorized to offer multiple so- cial, motivational, behavioral, metacognitive, and academic benefits. O’Donnell (2006) ob- served that the varied models of peer-supported learning are founded on theories emphasizing sociomotivational or cognitive aspects of the collaborative process.

Sociomotivationally grounded approaches to cooperative learning highlight the role of positive interdependence among group mem- bers and individual accountability of each mem- ber. These approaches lead to forming groups that are heterogeneous in ability, gender, and ethnicity, and suggest teachers set goals that require students to work together. For example, Slavin (1996) developed types of cooperative learning in which the whole group is rewarded for each of its members’ gains in performance, thus incentivizing mutual support for learning within the group. In what he called the social cohesion approach (e.g., Johnson & Johnson 1991), small groups work on developing social skills, concern for others, and giving productive feedback and encouragement. In this approach, group members take on predefined roles (e.g., note keeper), and the teacher assigns a single grade for the group’s work to reduce intragroup competition and promote positive interdependence.

Moderate achievement benefits arise from types of peer-supported learning that include positive interdependence, particularly in the form of interdependent reward contingencies (Rohrbeck et al. 2003, Slavin 1996). Using structured roles, as advocated by social cohe- sion theorists, appears to have little or no ef- fect on achievement (Rohrbeck et al. 2003) but may boost students’ social competence and self- concept (Ginsburg-Block et al. 2006). Peer- supported learning interventions are particu- larly effective in boosting achievement, social competence, self-concept, and task behavior among urban, low-income, minority students (Ginsburg-Block et al. 2006, Rohrbeck et al. 2003). Cooperative tasks designed to enhance student autonomy, such as allowing students to select goals and monitor and evaluate perfor- mance, enhance social skills, self-concept, and

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achievement. A plausible but unresearched hy- pothesis is that practicing metacognitive con- trol at the group level may help internalize metacognitive control at the individual level.

Cognitive theories of peer-supported learn- ing claim it strengthens individual students’ cognitive and metacognitive operations more than solo learning. Peer-supported learning is thought to offer more opportunities for re- trieving and activating schemas, elaborating new knowledge, self-monitoring, and exercis- ing metacognitive control (O’Donnell 2006). For example, using a method called guided reciprocal peer questioning (King 2002), a teacher might present a list of generic ques- tion stems such as “How does . . . affect . . . ?” and invite students to use the question stems to generate topic-relevant questions they can pose within their small group or dyad. Students can also learn to pose metacognitive questions, such as “How do you know that?” Having pairs of elementary students generate questions from cognitive question stems can enhance learning outcomes (King 1994, King et al. 1998), but the efficacy of metacognitive prompting by peers is less certain.

A student who helps another by generat- ing an explanation often learns more from the exchange than does the student who receives the explanation (Webb & Palincsar 1996). In research investigating why only some students who need help benefit from explanations, Webb & Mastergeorge (2003) described several qual- ities of successful help-seekers. They persisted in requesting help until they obtained expla- nations they understood. They attempted to solve problems without assistance and asked for specific explanations rather than answers to problems. These students adopted difficult but productive standards for monitoring and controlling learning. Classroom observations by Webb et al. (2008) indicate that teachers in primary grades can substantially increase the quality and quantity of explanations peers gen- erate in collaborative groups by encouraging them to request additional explanations that ex- tend or clarify an initial explanation. From the perspective of SRL, teachers who provide such

encouragements are leading students to set higher standards for metacognitive monitoring.

In Piagetian terms, equal-status peer inter- actions are more likely to trigger cognitive dis- equilibrium, thus engendering more engaged cooperation than do adult-child interactions (De Lisi 2002). After exposure to peers’ differ- ing beliefs, dialogue can develop a new under- standing that restores equilibrium. In Piaget’s theory, this process is hindered if collabora- tors have unequal status, as in adult-child inter- actions, because the higher-status participant is less likely to be challenged, and the lower- status participant tends to accept the other’s beliefs with little cognitive engagement. In other words, this is a form of self-handicapping metacognitive monitoring and control. In con- trast, Vygotsky (1978) held that children con- struct knowledge primarily by internalizing in- teractions with a more capable participant who adjusts guidance to match the less capable par- ticipant’s growing ability. This calls for sophisti- cated monitoring of a peer’s understanding and sensitive metacognitive control that is gradu- ally released to the developing learner. Studies of learning gains by children who collabora- tively solved problems without external feed- back found that among children paired with a lower-ability, similar-ability, or higher-ability partner, only those paired with a higher-ability partner tended to benefit from collaboration (Fawcett & Garton 2005, Garton & Pratt 2001, Tudge 1992). Tudge (1992) found that the members of similar-ability dyads were at risk of regressing in performance as a result of col- laboration. These results favor Vygotsky’s over Piaget’s account of how status among collabo- rators stimulates knowledge construction.

How can learners of nearly equal knowledge and ability benefit from collaboration? How can more-capable children adjust help given to meet a peer’s needs when they may be unable to monitor even their own abilities? Answers may lie in cognitive strategy instruction in which (a) the teacher guides and models group inter- actions and (b) students are assigned to roles that require metacognitive monitoring (Palincsar & Herrenkohl 2002). This approach is best

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reflected in research on reciprocal teaching to improve the reading comprehension of below- average readers. Here, the teacher’s role grad- ually shifts from direct explanation and model- ing to coaching group interactions. A review of quantitative studies found that reciprocal teach- ing is consistently more effective than are meth- ods in which teachers lead students in read- ing and answering questions about text passages (Rosenshine & Meister 1994).

For social-cognitive theorists, collaboration is an academic context to which individu- als bring personal efficacy and achievement goals. Surprisingly, there is a lack of social- cognitive research on peer-supported learn- ing (Pintrich et al. 2003). This is not because social-cognitive theories have no implications for collaborative learning. As an example, stu- dents who have performance avoidance goals and low personal efficacy are less likely to seek help from teachers and are theoretically also less willing to seek help from peers (Webb & Mastergeorge 2003). These students monitor collaborations using standards that handicap learning or lack skills for interacting with peers in more productive ways. At a more fundamen- tal level, Bandura (2000) argued human groups manifest a collective efficacy, the members’ per- ceptions of the efficacy of the group. Because collective efficacy is interdependent with group performance and the personal efficacy of its members, it has potentially important but un- explored implications for peer-supported learn- ing. These and other unexamined implications of sociocognitive theory are opportunities to elaborate peer-supported learning in terms of metacognitive monitoring and control.

Research has offered only weak accounts of the many opportunities for metacognitive monitoring and control in peer-supported learning, including soliciting and giving ex- planation, sharing appropriate schemas, and using appropriate standards for monitoring progress. Feldmann & Martinezpons (1995) found that individual self-regulation beliefs predicted collaborative verbal behavior and individual achievement. However, there is little evidence that self-regulatory ability improves

collaboration and, if so, which aspects of self-regulation affect qualities of collaboration that recursively promote academic achieve- ment. In what is perhaps the most informative research in this area, low-achieving students were induced to approach a collaborative problem-solving activity with either learning or performance goals as standards for mon- itoring interactions (Gabriele 2007). Those with a learning goal demonstrated higher comprehension monitoring, more constructive collaborative engagement, and higher posttest performance. Without further research like this, the role played by metacognitive moni- toring and control in peer-supported learning will remain obscure.

Classrooms and Class Size

The relationship between class size and student achievement has been widely studied. This issue is so alluring it has attracted researchers even from economics and sociology. Smith & Glass’s (1980) meta-analysis established that reducing class size tends to raise students’ achievement in a nonlinear relationship. Removing one stu- dent from a class of thirty tends to raise the class mean far less than removing one student from a class of two. In textbooks and thumbnail reviews, the nonlinearity of the effect is usually reduced to a simpler principle: gains in achieve- ment are achieved when class size falls to 15 students or fewer.

Project STAR (Student Teacher Achieve- ment Ratio), a large-scale experiment on class size, is lauded as one of the most significant educational investigations ever conducted (Mosteller 1995). The project randomly assigned approximately 12,000 Tennessee elementary school students and their teachers to small (13–17 students) and regular-sized (22–25 students) classes. The students entered the experiment in kindergarten, grade 1, grade 2, or grade 3. Although the intervention ended after grade 3, achievement data were collected until grade 9. In one analysis of the STAR data, Krueger (1999) concluded that students in their first year of small classes scored an average of

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4 percentiles higher and increased that advan- tage in subsequent years of small classes by about 1 percentile per year. This analysis offers limited value to policy makers because the cost of reducing class sizes by one third is high, and other interventions are known to produce larger effects. Even more concerning is that the benefits of some educational interven- tions diminish rapidly after the intervention terminates.

Fortunately, a more-detailed picture has emerged from the STAR data. Krueger (1999) reported that low-socioeconomic-status (SES) students, African American students, and inner- city students all benefited from small class sizes more than did the general population. Evi- dence has also emerged that benefits obtained from small class sizes in grades K–3, including the extra gains for disadvantaged groups, per- sisted until at least grade 8 (Nye et al. 2004). There is an important complication: Small class sizes tend to increase variability in achievement and expand the gap between the highest- and lowest-achieving students (Konstantopoulos 2008). Still more challenging is that re- cent observational research reports no positive achievement effects from small class sizes in kindergarten (Milesi & Gamoran 2006).

Research relating class size and demo- graphic variables to achievement fails to explain how learning is affected. Looking inside the black box of class size could shine light on this mystery. Blatchford and colleagues (2002, 2007) conducted a series of systematic observations in England of teaching and learning in small and regular-sized classrooms for students ages 11 and under. They found that children in small classes interacted more with their teachers, re- ceived more one-to-one instruction, and paid more attention to their teachers (Blatchford et al. 2002, 2007). Teachers and observers in small classes reported that more time was allo- cated to assessing individual student products and progress. Despite these impacts on teach- ing, Blatchford et al. (2007) concluded teachers may not take full advantage of reduced class size. They often persisted with more whole-class in- struction than necessary and failed to adopt

cooperative learning strategies that become more feasible in smaller classes.

This is consistent with conclusions of the STAR project. On the whole, teachers assigned to smaller classes did not strategically modify their teaching (Finn & Achilles 1999). Indeed, taking a sociological perspective, Finn et al. (2003) proposed that improved learning out- comes in small classes are strongly mediated by students’ sense of belonging and their academic and social engagement. Students’ choices about how they learn and teachers’ choices about how they teach are manifestations of metacognitive control. These choices are shaped by standards they each use to metacognitively monitor their circumstances and themselves. In short, stan- dards matter. How do students and teachers ac- quire them, search for and select them, and use them in these situations?

If resources are allocated to decreasing class sizes in the early grades, how can administrators and teachers know when students are ready to learn in larger classrooms, where they have less teacher support? We speculate that students’ abilities to independently monitor and regulate their learning are crucial to successful perfor- mance in larger classes. We recommend devel- oping performance-based tools to assess when children have self-regulating skills for learning where there is less teacher attention.

Homework

In her article “Homework is a Complicated Thing,” Corno (1996) described difficulties in forming widely applicable, evidence-based homework policies. Corno’s title is still the best one-line summation of what is known about the psychology of homework. This is yet another case illustrating that hundreds of investigations using a variety of methods have only weakly in- formed teaching practices and policy, perhaps because these studies failed to consider learners as metacognitive agents.

Teachers assign readings, problem sets, re- ports, and projects as homework for a vari- ety of instructional purposes, including prac- ticing skills demonstrated in class, preparing

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for class discussions, and creatively integrat- ing and applying knowledge acquired from multiple sources (Epstein & Van Voorhis 2001). Homework also may be assigned with intentions to develop time-management and other self-regulatory skills, stimulate parental involvement, and foster parent-teacher communication.

Historically, homework has been controver- sial. Periodic calls to abolish it are grounded in claims that it is instructionally ineffective and pulls time away from family activities. Calls for abolishing homework interleave with calls for assigning more homework to increase children’s preparation for a knowledge-based, competitive world. Homework can be misused when teachers assign too much or use it to pun- ish (Corno 1996). In investigating links between stress and homework, Kouzma & Kennedy (2002) found Australian senior high school students reported a mean of 37 hours of home- work per week. Time spent on homework cor- related with self-reported mood disturbance. Advocates for educational equity have claimed that homework can increase the performance gap between high- and low-achieving students (McDermott et al. 1984).

The relationship between homework and academic achievement is most fully mapped in two landmark meta-analyses (Cooper 1989, Cooper et al. 2006). Cooper (1989) set out a detailed model of homework effects that in- cludes (a) exogenous factors such as student ability and subject matter, and assignment char- acteristics such as amount and purpose; (b) class- room factors, such as the provision of materials; (c) home-community factors, such as activities competing for student time; and (d ) classroom follow-up factors, such as feedback and uses of homework in class discussions. The strongest evidence for homework’s efficacy comes from intervention studies, some using random as- signment, in which students were or were not given homework. Cooper’s meta-analyses sta- tistically detected advantages due to homework in these studies, with weighted mean effect sizes for student test performance of d = 0.60 (Cooper et al. 2006) and d = 0.21 (Cooper

1989). In a review of studies correlating self- reported time spent on homework and achieve- ment, Cooper et al. (2006) statistically de- tected a positive weighted average effect size of r = 0.25 for high school students but did not detect an effect for elementary students. They reported some evidence of a curvilinear rela- tionship between amount of homework and performance. In Lam’s study of grade 12 stu- dents cited by Cooper et al. (2006), the benefit from homework was strongest for students do- ing 7 to 12 hours of homework per week and weakest for students doing more than 20 or less than 6 hours per week.

Trautwein and colleagues (Trautwein 2007, Trautwein et al. 2009) argued that homework is a “classic example of the multi-level problem” whereby generally positive effects of homework reported in Cooper’s meta-analyses mask con- siderable underlying complexity. Working with data from 1275 Swiss students in 70 eighth- grade classes, they distinguished three levels of analysis. At the class level, they found a positive relationship between the frequency of home- work assigned by teachers and classes’ achieve- ment. At the between-individual level, achieve- ment related positively to students’ homework effort but negatively to homework time. At the intraindividual level, in which students were assessed longitudinally, the time-achievement effect flipped direction—homework time related positively to achievement.

Cooper and Trautwein and their colleagues call for better-designed and more-ambitious research on homework. As in so many ar- eas of educational research, there is a need for large-scale experiments, longitudinal ob- servations, hierarchical analyses, and improved methods for gathering qualitative, time-on- task, and fine-grained data that trace cognitive processes. Research also is needed on the ef- fects of potentially moderating variables such as culture, grade level, subject area, cognitive ability, and the manifold factors identified in Cooper’s model. Finally, there is a need to de- velop and investigate innovative homework ac- tivities and compare them with conventional forms of homework.

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Alongside these macro-level relations, we theorize self-regulation is a key factor in de- termining the effects of homework activities. Here, there is a dearth of research. In one observational study, Zimmerman & Kitsantas (2005) found that homework experiences pos- itively predicted secondary students’ sense of personal responsibility and self-efficacy be- liefs, including self-monitoring and organizing. Those beliefs predicted academic achievement. In research on the other side of the recip- rocal relationship, training in homework self- monitoring was equally effective as parental monitoring in raising homework-completion rates above those of a no-intervention control group (Toney et al. 2003).

Socioeconomic Status

In educational research, SES is most commonly measured by a composite of parents’ educa- tion, occupation, and income. Despite older, widespread beliefs about its overwhelming pre- dictive power, SES is only a moderately strong predictor (relative to other known factors) of school achievement in the United States (White 1982). The most recent meta-analysis of U.S. studies found correlations between SES and achievement of 0.23 to 0.30 when measured at the student level (Sirin 2005). By comparison, this effect size is about the same as the meta-analytically derived correlation between parental involvement and achieve- ment (Fan & Chen 2001) and considerably weaker than correlations of achievement with educational resources available in the home (r = 0.51) (Sirin 2005) and parental attitudes toward education (r = 0.55) (White 1982). Internationally, the effects of SES are pervasive and operate both within and between countries (Chiu & Xihua 2008).

Determining which factors mediate the relationship between SES and students’ achievement is challenging because the relevant research is observational, and data range in lev- els from the student to whole countries. Using multilevel modeling of data from 25 countries, Park (2008) investigated the role of the home literacy environment (early home literacy

activities, parental attitudes toward reading, and number of books at home) in mediating the relationship between parental education and reading performance. He found the home literacy environment strongly predicted reading achievement even after statistically controlling for parental education, but it only partially mediated the relationship between parental education and reading performance.

Another factor that may account for bet- ter reading performance by higher-SES chil- dren is orally transmitted vocabulary. A U.S. study (Farkas & Beron 2004) found a gap be- tween the oral vocabulary of high- and low- SES children by three years of age, but this did not increase after children entered kinder- garten. This suggests that school helps equalize prior differences between children from differ- ent socioeconomic backgrounds. A structural equation modeling study found that parent- led home learning experiences (e.g., reading, games, and trips to the zoo or park) medi- ated the relationship between SES and liter- acy (Foster et al. 2005). We have not found research investigating the relationship between SES and metacognitive monitoring and control and whether these skills mediate the effects of SES on achievement. Thus, a full explanation of how SES affects learning is not available.

In summary, low SES appears to create significant but not insurmountable barriers to achievement in elementary school and beyond. The effects of SES are likely mediated by fac- tors such as educational resources available in the home, parental aspirations for their chil- dren’s education, home literacy activities, and parental transmission of oral vocabulary. More high-quality research is needed to investigate the most effective types of interventions for low-SES children, especially whether programs that develop metacognitive and self-regulatory skills could reduce the disadvantages they face.

PERSISTENT DEBATES

Learning and Cognitive Styles

We have never met a teacher who held that teaching is maximally successful when all

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learners are taught identically. The opposite view—that teaching should adapt to learn- ers’ individual differences—requires identify- ing one or more qualities of learners upon which to pivot features of instruction. One class of such qualities is styles.

Allport (1937) is credited with introducing the phrase “cognitive style” to describe peo- ple’s preferred or customary approaches to per- ception and cognition. When situations involve learning, stylistic approaches are termed “learn- ing styles” (Cassidy 2004).

In an early paper, Messick (1970) distin- guished nine cognitive styles. More recently, Coffield et al. (2004) cataloged 71 different models grouped into 13 families. Kozhevnikov (2007) classified 10 major groupings. Sternberg et al. (2008) collapsed all these into two cate- gories. Ability-based styles characterize the typ- ical approach(es) a learner takes in achievement tasks, such as representing givens in a prob- lem using symbolic expressions or diagrams. Personality-based styles describe a learner’s preference(s) for using abilities. Typical and preferred approaches may or may not match.

A recent theoretical synthesis (Kozhevnikov 2007) described styles as “heuristics [that] can be identified at each level of information pro- cessing, from perceptual to metacognitive. . . [whose] main function is regulatory, control- ling processes from automatic data encoding to conscious allocation of cognitive resources.” Very few studies are researching this view. The vast majority of research in educational set- tings aligns with Messick’s (1984) view that styles “are spontaneously applied without con- scious consideration or choice across a wide variety of situations” (p. 61). Therefore, stud- ies have mainly developed and contrasted self- report inventories or explored correlates of styles while attempting to show that match- ing styles to forms of instruction has benefits while mismatching does not. Learners often can reliably describe themselves as behaving stylistically. Their reports correlate moderately with various demographic variables, individ- ual differences, and achievement (e.g., Watkins 2001, Zhang & Sternberg 2001). Contrary to

expectations, matching instruction to style does not have reliable effects (Coffield et al. 2004).

There are challenges to using styles in psy- chological accounts of school performance. First, thorough and critical syntheses of the psy- chometric properties and validity of self-report style measures are scant. One of the few was Pittenger’s (1993) review of the Myers-Briggs Type Indicator. He concluded, “. . .there is no convincing evidence to justify that knowledge of type is a reliable or valid predictor of impor- tant behavioral conditions” (p. 483). Second, studies investigating the match of self- reports to behaviors are also rare. Krätzig & Arbuthnott’s (2006) study of visual, auditory, kinesthetic, and mixed learning styles found no correlation between self-reported preferences for styles and objective scores on cognitive tasks measuring what the style was about. The study of field dependency-independence by Miyake et al. (2001) led them to conclude that this style “should be construed more as a cognitive abil- ity, rather than a cognitive style” (p. 456).

Discovery Learning

Discovery learning is most strongly associated with science and math education. It has roots in the Piagetian view that “each time one prema- turely teaches a child something he could have discovered for himself, that child is kept from inventing it and consequently from understand- ing it completely” (Piaget 1970, p. 715). Bruner (1961) theorized that discovery learning fosters intrinsic motivation, leads to an understand- ing of and inclination toward the heuristics of inquiry, and allows for the active self- organization of new knowledge in a way that fits the specific prior knowledge of the learner. According to Hammer (1997, p. 489), discovery learning usually “refers to a form of curriculum in which students are exposed to particular questions and experiences in such a way that they ‘discover’ for themselves the intended concepts.” In unguided and minimally guided discovery learning, the role of the teacher is constrained to providing a learning environ- ment or problem space and perhaps posing

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questions. In discovery learning, teacher-posed questions should lead the student toward Piagetian disequilibrium, which is conceived as cognitive conflict between prior knowledge and new information from the environment.

Proponents of discovery learning believe it produces highly durable and transferable knowledge, a claim consistent with some ob- servational evidence. For example, children in grades one and two who spontaneously in- vented and used arithmetic strategies subse- quently showed greater understanding of base 10 number concepts and better performance on transfer problems than did children who initially acquired the standard arithmetic algo- rithms from instruction (Carpenter et al. 1998).

In a widely cited review, Mayer (2004) criticized discovery methods that emphasize unguided exploration in learning environments and problem spaces. Describing a belief in the value of pure discovery learning as “like some zombie that keeps returning from its grave” (p. 17), he reviewed investigations in three domains—problem-solving rules, conservation strategies, and Logo programming strategies. Mayer (2004) observed how in each case, ac- cumulated evidence favored methods in which learners received guidance. He questioned the supposed connection between discovery teaching methods and constructivist theories, arguing that cognitive activity, not behavioral activity, is the essential requirement for con- structivist learning. He maintained that, as a consequence, “active-learning” interventions such as hands-on work with materials and group discussions are effective only when they promote cognitive engagement directed toward educational goals.

The debate often pits discovery learning against direct instruction. Direct instruction is a broad domain of explicit teaching prac- tices that include stating learning goals, review- ing prerequisite knowledge, presenting new information in small steps, offering clear in- structions and explanations, providing opportu- nity for frequent practice, guiding performance, and giving customized, explanatory feedback (Rosenshine 1987). Originating as an approach

to teaching primary reading, direct instruc- tion has been successful within a wide range of general- and special-education programs at the elementary level (Swanson & Hoskyn 1998).

Discovery learning has been seen as a tool for acquiring difficult, developmentally signif- icant knowledge, such as the control of vari- ables strategy (CVS) used in designing experi- ments. However, when Klahr & Nigam (2004) randomly assigned elementary students to learn CVS by discovery or direct instruction, many more succeeded in the direct-instruction condi- tion. Moreover, on an authentic transfer task in- volving evaluating science fair posters, the many students in the direct instruction condition who showed success while learning performed as well as the few students in the discovery group who also showed success while learning. Dean & Kuhn (2007) randomly assigned students learning CVS to direct instruction, discovery learning, and a combination of the two. Direct instruction was presented only during an initial session, and the discovery learning treatment extended over 12 sessions. In this study, direct instruction produced an immediate advantage, which disappeared in a posttest and a transfer task given several weeks after the termination of the discovery learning sessions. Although both of these experiments implemented direct in- struction as a single session in which CVS was presented and modeled by a teacher, the experi- ments failed to include teacher-guided practice with feedback, which is a powerful and essential component of direct instruction.

A review by Kirschner et al. (2006) explained the evidence against minimally guided instruc- tion in terms of cognitive load theory. They cast discovery learning as a type of problem solving that requires a cognitively demanding search in a problem space. According to cogni- tive load theory, such a search is extrinsic load that requires time and cognitive resources that otherwise could be used for understanding and elaborative processing of solution schemas. To support this claim, they cited evidence that novices learn to solve problems more effec- tively by initially studying worked solutions

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before starting to solve problems (Tuovinen & Sweller 1999).

Rittle-Johnson (2006) pointed out that dis- covery learning theorists tend to conflate the two separate cognitive processes of reasoning about solutions and inventing them. She did a 2 × 2 experiment in which elementary school children learning the concept of mathematical equivalence were assigned to either instruction or invention and either self-explanation or no self-explanation. The invention condition of- fered no advantages. Both instruction and self- explanation conditions produced advantages for procedural learning on a delayed posttest, and only self-explanation produced advantages for transfer. It may be that self-directed elaborative processing, in this case manifested as self-explanation, is the only way to obtain high-level transfer (Salomon & Perkins 1989). The search of the problem space entailed by unguided discovery may hinder high-level transfer by taxing cognitive resources.

Another explanation of evidence favor- ing guided instruction is that students lack metacognitive skills needed to learn from unguided exploration. They may be unable to manage time to explore all relevant possibil- ities, keep track of which conditions and cases they have already explored, accurately monitor what they know and need to know, and monitor what works over the course of learning.

There is a need for better theory and evidentiary support for principles of guided discovery. We recommend investigating mul- tiple ways of guiding discovery so that, ideally, every child is led to the brink of invention and extensive search of the problem space is avoided. Metacognitive guidance could include suggestions to generate a hypothesis, to make a detailed action plan, and to monitor the gap between the research question and the obser- vations. These cognitive and metacognitive activities improve learning outcomes (Veenman et al. 1994).

The timing of metacognitive guidance may be critical. Hulshof & de Jong (2006) provided “just-in-time” instructional tips in a computer- based environment for conducting simulated

optics experiments. A new tip became acces- sible every three minutes and could be con- sulted at any time thereafter. Although consult- ing the tips was optional, and tips contained no information that was directly assessed by the posttest, students randomly assigned to a condi- tion that provided the tips outperformed peers in a control condition on the posttest. A poten- tial drawback to this type of optional support is that students may misjudge their need for guid- ance and fail to access a needed tip or make excessive use of tips to avoid genuine cogni- tive engagement with the problem (Aleven et al. 2003). Theories about guided learning that may emerge from such research should strive to account for the motivational, cognitive, and metacognitive factors reviewed in this article.

METHODOLOGICAL ISSUES IN MODELING A PSYCHOLOGY OF ACADEMIC ACHIEVEMENT

Paradigmatic Issues

The psychology of school achievement has been studied mainly within a paradigm that we suggest faces difficult challenges. Intending no disrespect, we call this the “snapshot, bookend, between-groups paradigm”—SBBG for short. Recall Roediger’s (2008) conclusion that the “only sort of general law, is that in making any generalization about memory one must add that ‘it depends’” (p. 247). We posit that his claim generalizes to most if not all findings in a psy- chology about the way things are because of rules for doing research according to the SBBG paradigm.

SBBG is snapshot because data that reflect the effect of a causal variable almost always are collected just once, after an intervention is over. We acknowledge some studies are longitudinal but maintain that snapshot studies overwhelm- ingly form the basis of today’s psychology of academic achievement.

Beyond the shortcoming of insufficiently tracing events between the bookends of a learning session, there is another reason that educational psychology’s snapshot-oriented

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research paradigm may model academic achievement incompletely. Students in class- rooms and people in training learn new infor- mation and shift motivation and affect across time. A snapshot study captures just one posttest or pre-to-post segment within a longer trajectory of psychological events. The field has insufficiently attended to how segments con- catenate. This is a necessary concern in mod- eling a trajectory of learning because the next segment may not match a researcher’s predicted concatenation. But this issue is not one to vali- date analytically and a priori. Data are required to characterize how, at any point in the trajec- tory of a learning activity, a learner metacogni- tively monitors and exercises the metacognitive control that forms a trajectory of learning.

SBBG is a bookend paradigm because re- searchers rarely gather data representing proxi- mally cognitive or motivational events between the time when learners are randomly assigned to an intervention and the time when potential effects are measured after the intervention is over. Ideally, random assignment reduces the necessity to gather data before an intervention. (But see Winne 2006 for an argument about challenges to random assignment as a panacea for erasing extraneous variance.) Otherwise, premeasures are secured to reduce “error” vari- ance by blocking or statistically residualizing the outcome variable. (But see Winne 1983 for challenges to interpretation that arise in this case.) Random assignment and premeasures cannot identify cognitive processes that create changes in achievement. Randomness cannot help researchers interpret a systematic effect. Change in a learner’s achievement can be conditioned by an aptitude that remains con- stant for that learner during the intervention, but that change cannot be caused unless this aptitude varies during the intervention.

An alternative that could illuminate achievement-changing processes inside an intervention is to gather data to proximally trace those processes (Borsboom et al. 2003, Winne 1982). Regrettably, data of this kind are rarely gathered because it is impractical. (But see Winne 2006 for ideas about how

impracticalities might be overcome using software technologies.) Thus, in bookend ex- periments, psychological processes that unfold as learners experience the intervention must be inferred rather than validated using fine- grained data gathered over time between the experiment’s bookends (Winne & Nesbit 2009). Traces of processing allow opening the book between a traditional experiment’s bookends and viewing each “page” situated in relation to prior events and following events. This allows merging psychologies of “the way things are” with “the way learners make things.” Modeling should honor the dual role of events observed at points within the intervention, first as the outcome of prior psychological process and second as a process that generates the next state. Empirically investigating a learning trajectory, therefore, entails gathering data that can more fully contribute to accounting for change over time. This stands in contrast to data that reflect only the cumulative products of multiple processes that unfold over time with an intervention.

SBBG is a between-groups paradigm be- cause it forces interpretations about whether an intervention changes learners’ achievement to be grounded in differences (variance) be- tween the central tendencies of a treatment group versus a comparison group. Data are lacking that trace how learners make things. Therefore, variance within each group due, in part, to individuals’ self-regulating learning— metacognitive monitoring and control applied “on the fly” —has to be treated as “residual” or “error.” In fact, the epitome of an experiment in the between-groups tradition would zero out individual differences in the ways learners make things.

If learners are agents, this approach leaves out key parts of the story about how achievement changes. The between-groups experimental approach relieves this tension by explaining effects in terms of a psychological process that does not vary across individuals despite researchers’ belief in variance in the way learners make things. Thus, without opening the book of each group member’s experience,

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“between-subjects models do not imply, test, or support causal accounts that are valid at the individual level” (Borsboom et al. 2003, p. 214). The result is that a psychology about the way things are becomes an “it depends” science because between-groups experiments must neglect causal effects that arise from individual differences in the way learners make things.

A Revised Paradigm

We suggest that a more productive psychol- ogy of academic achievement should probe and map how learners construct and use informa- tion within boundaries set by the way things are. This entails three major paradigmatic changes. First, gather data that trace variance in learn- ers’ psychological states over time during an in- tervention. Supplement snapshot data. Second, conceptualize trajectories of learning as a suc- cession of outcomes reciprocally determined by learners who choose information and modes of processing it to construct successive infor- mational products. Read between bookends. Third, in the many situations where random assignment is not feasible and even where it is, define groups of learners a posteriori in terms of trace data that prove learners to be approx- imately homogenous in their information pro- cessing. Fix causes at the individual level, then explore for mediating and moderating vari- ables post hoc. A paradigm that includes tracing agents’ self-regulated processes provides raw materials that can support grounded accounts of what happens in the psychology of academic achievement at the same time it accommodates variations in instructional designs.

SHAPES FOR FUTURE RESEARCH

We judge that the field of educational psy- chology is in the midst of striving to integrate two streams. One stream investigates whether achievement improves by manipulating instruc- tional conditions (e.g., class size, discovery learning) or accommodating trait-like individ- ual differences (e.g., epistemic beliefs) or social conditions (e.g., SES). In these studies, what

individual learners do inside the span of a learn- ing session and how each learner adjusts goals, tactics, and perceptions have been of interest. But these generating variables have rarely been directly operationalized and, when acknowl- edged, they are mostly treated as error variance terms in analyses of data. The second stream of studies seeks to operationalize reciprocally de- termined relations among a learner’s metacog- nition, broadly conceptualized, and outcomes. In these studies, bookend variables set a stage of movable props: standards for metacognitively monitoring and choices exercised in metacogni- tive control. Learners choose the information- processing tools they use within bounds of a psychology of the way things are.

We take as prima facie that changes in aca- demic achievement have origins in psycholog- ical phenomena. Snapshot, bookend between- groups studies in educational psychology have not traced those phenomena, as Winne (1983) and Borsboom et al. (2003) argued. Educational psychology should turn its attention to methods that penetrate correlations among distal vari- ables. The goal should be to develop maps of proximal psychological processes that reflect causes of learning. In doing so, we hypothe- size research must concern itself with learn- ers’ metacognitive monitoring and control. These processes set into motion forms of self- regulated learning that have been demonstrated to influence achievement. Studies should be not only more intensely focused on proximal indi- cators of psychological processes; researchers also need to gather data inside the bookends of learning sessions to track reciprocally de- termined relations that shape learning trajec- tories. In short, we recommend that snapshot, bookend between-groups research be comple- mented with a microgenetic method (Siegler & Crowley 1991). This suggests several require- ments. One is operationally defining traces to describe which psychological processes in the realm of “the way things are” are applied dur- ing learning. Another is determining which standards learners apply in their metacognitive monitoring that leads to metacognitive control. These data model the way learners make things.

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By a mix of natural exploration and instruc- tion, learners develop their own heuristics, re- flective of a naı̈ve psychology of the way things are, about how cognitive and external factors can be arranged to acquire and successfully use academic knowledge. As agents, they oper- ationalize those heuristics by metacognitively monitoring and controlling mental states and by manipulating external factors. By tracking their academic achievements and side effects over time, they become informed about how to regulate engagement in learning to improve the results of subsequent engagements. In short, over time, self-regulating learners experiment with learning to improve how they learn along- side what they learn (Winne 1995).

Findings from the psychology of the way things are will become better understood as we advance the psychology of how learners make things. This will involve learning more about standards that learners use to metacognitively monitor, the nature of monitoring per se, how learners characterize a profile of features gen- erated by monitoring, and how potential ac- tions are searched for and matched to a profile generated by monitoring that sets a stage for metacognitive control. Metaphorically, because

learners are in the driver’s seat, educational psy- chology needs a model of how learners drive to understand more fully how they reach desti- nations of academic achievement. By incorpo- rating metacognition and its larger-scale form, self-regulated learning, into data and analyses of data, rather than randomizing out these factors, we submit a psychology of academic achieve- ment can advance theoretically and offer more powerful principles for practice.

Our hypothesis is that gluing together the two psychologies of the way things are and the way learners make things will reduce the de- gree of Roediger’s “it depends” hedge on laws of memory (and learning). Two inherent sources of variance need examining: What do learners already know and access over the fine-grained course of a learning session? How do learners self-regulate learning across sessions to adapt in service of achieving their goals? Richer in- terpretations will need to be grounded on fine- grained trace data that fill in gaps about pro- cesses in learning, specifically: Which heuristics for learning do learners consider, choose, apply, and adapt? How do those processes by which learners make things and self-regulate unfold under constraints of how things are?

DISCLOSURE STATEMENT

The authors are not aware of any biases that might be perceived as affecting the objectivity of this review.

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Annual Review of Psychology

Volume 61, 2010 Contents

Prefatory

Love in the Fourth Dimension Ellen Berscheid � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Brain Mechanisms and Behavior

The Role of the Hippocampus in Prediction and Imagination Randy L. Buckner � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �27

Learning and Memory Plasticity; Neuroscience of Learning

Hippocampal-Neocortical Interactions in Memory Formation, Consolidation, and Reconsolidation Szu-Han Wang and Richard G.M. Morris � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �49

Stress and Neuroendocrinology

Stress Hormone Regulation: Biological Role and Translation Into Therapy Florian Holsboer and Marcus Ising � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �81

Developmental Psychobiology

Structural Plasticity and Hippocampal Function Benedetta Leuner and Elizabeth Gould � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 111

Cognitive Neuroscience

A Bridge Over Troubled Water: Reconsolidation as a Link Between Cognitive and Neuroscientific Memory Research Traditions Oliver Hardt, Einar Örn Einarsson, and Karim Nader � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 141

Cognitive Neural Prosthetics

Richard A. Andersen, Eun Jung Hwang, and Grant H. Mulliken � � � � � � � � � � � � � � � � � � � � � � 169

Speech Perception

Speech Perception and Language Acquisition in the First Year of Life Judit Gervain and Jacques Mehler � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 191

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Chemical Senses (Taste and Smell)

An Odor Is Not Worth a Thousand Words: From Multidimensional Odors to Unidimensional Odor Objects Yaara Yeshurun and Noam Sobel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 219

Somesthetic and Vestibular Senses

Somesthetic Senses Mark Hollins � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 243

Basic Learning and Conditioning

Learning: From Association to Cognition David R. Shanks � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 273

Comparative Psychology

Evolving the Capacity to Understand Actions, Intentions, and Goals Marc Hauser and Justin Wood � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 303

Human Development: Processes

Child Maltreatment and Memory Gail S. Goodman, Jodi A. Quas, and Christin M. Ogle � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 325

Emotional, Social, and Personality Development

Patterns of Gender Development Carol Lynn Martin and Diane N. Ruble � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 353

Adulthood and Aging

Social and Emotional Aging Susan T. Charles and Laura L. Carstensen � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 383

Development in Societal Context

Human Development in Societal Context Aletha C. Huston and Alison C. Bentley � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 411

Genetics and Psychopathology

Epigenetics and the Environmental Regulation of the Genome and Its Function Tie-Yuan Zhang and Michael J. Meaney � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 439

Social Psychology of Attention, Control, and Automaticity

Goals, Attention, and (Un)Consciousness Ap Dijksterhuis and Henk Aarts � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 467

Contents vii

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Bargaining, Negotiation, Conflict, Social Justice

Negotiation Leigh L. Thompson, Jiunwen Wang, and Brian C. Gunia � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 491

Personality Development: Stability and Change

Personality Development: Continuity and Change Over the Life Course Dan P. McAdams and Bradley D. Olson � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 517

Work Motivation

Self-Regulation at Work Robert G. Lord, James M. Diefendorff, Aaron C. Schmidt, and Rosalie J. Hall � � � � � � � � 543

Cognition in Organizations

Creativity Beth A. Hennessey and Teresa M. Amabile � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 569

Work Attitudes ( Job Satisfaction, Commitment, Identification)

The Intersection of Work and Family Life: The Role of Affect Lillian T. Eby, Charleen P. Maher, and Marcus M. Butts � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 599

Human Factors (Machine Information, Person Machine Information, Workplace Conditions)

Cumulative Knowledge and Progress in Human Factors Robert W. Proctor and Kim-Phuong L. Vu � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 623

Learning and Performance in Educational Settings

The Psychology of Academic Achievement Philip H. Winne and John C. Nesbit � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 653

Personality and Coping Styles

Personality and Coping Charles S. Carver and Jennifer Connor-Smith � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 679

Indexes

Cumulative Index of Contributing Authors, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � 705

Cumulative Index of Chapter Titles, Volumes 51–61 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 710

Errata

An online log of corrections to Annual Review of Psychology articles may be found at http://psych.annualreviews.org/errata.shtml

viii Contents

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AnnuAl Reviews it’s about time. Your time. it’s time well spent.

AnnuAl Reviews | Connect with Our experts Tel: 800.523.8635 (us/can) | Tel: 650.493.4400 | Fax: 650.424.0910 | Email: [email protected]

New From Annual Reviews: Annual Review of Organizational Psychology and Organizational Behavior Volume 1 • March 2014 • Online & In Print • http://orgpsych.annualreviews.org

Editor: Frederick P. Morgeson, The Eli Broad College of Business, Michigan State University The Annual Review of Organizational Psychology and Organizational Behavior is devoted to publishing reviews of the industrial and organizational psychology, human resource management, and organizational behavior literature. Topics for review include motivation, selection, teams, training and development, leadership, job performance, strategic HR, cross-cultural issues, work attitudes, entrepreneurship, affect and emotion, organizational change and development, gender and diversity, statistics and research methodologies, and other emerging topics.

Complimentary online access to the first volume will be available until March 2015. TAble oF CoNTeNTs: • An Ounce of Prevention Is Worth a Pound of Cure: Improving

Research Quality Before Data Collection, Herman Aguinis, Robert J. Vandenberg

• Burnout and Work Engagement: The JD-R Approach, Arnold B. Bakker, Evangelia Demerouti, Ana Isabel Sanz-Vergel

• Compassion at Work, Jane E. Dutton, Kristina M. Workman, Ashley E. Hardin

• Constructively Managing Conflict in Organizations, Dean Tjosvold, Alfred S.H. Wong, Nancy Yi Feng Chen

• Coworkers Behaving Badly: The Impact of Coworker Deviant Behavior upon Individual Employees, Sandra L. Robinson, Wei Wang, Christian Kiewitz

• Delineating and Reviewing the Role of Newcomer Capital in Organizational Socialization, Talya N. Bauer, Berrin Erdogan

• Emotional Intelligence in Organizations, Stéphane Côté • Employee Voice and Silence, Elizabeth W. Morrison • Intercultural Competence, Kwok Leung, Soon Ang,

Mei Ling Tan • Learning in the Twenty-First-Century Workplace,

Raymond A. Noe, Alena D.M. Clarke, Howard J. Klein • Pay Dispersion, Jason D. Shaw • Personality and Cognitive Ability as Predictors of Effective

Performance at Work, Neal Schmitt

• Perspectives on Power in Organizations, Cameron Anderson, Sebastien Brion

• Psychological Safety: The History, Renaissance, and Future of an Interpersonal Construct, Amy C. Edmondson, Zhike Lei

• Research on Workplace Creativity: A Review and Redirection, Jing Zhou, Inga J. Hoever

• Talent Management: Conceptual Approaches and Practical Challenges, Peter Cappelli, JR Keller

• The Contemporary Career: A Work–Home Perspective, Jeffrey H. Greenhaus, Ellen Ernst Kossek

• The Fascinating Psychological Microfoundations of Strategy and Competitive Advantage, Robert E. Ployhart, Donald Hale, Jr.

• The Psychology of Entrepreneurship, Michael Frese, Michael M. Gielnik

• The Story of Why We Stay: A Review of Job Embeddedness, Thomas William Lee, Tyler C. Burch, Terence R. Mitchell

• What Was, What Is, and What May Be in OP/OB, Lyman W. Porter, Benjamin Schneider

• Where Global and Virtual Meet: The Value of Examining the Intersection of These Elements in Twenty-First-Century Teams, Cristina B. Gibson, Laura Huang, Bradley L. Kirkman, Debra L. Shapiro

• Work–Family Boundary Dynamics, Tammy D. Allen, Eunae Cho, Laurenz L. Meier

Access this and all other Annual Reviews journals via your institution at www.annualreviews.org.

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AnnuAl Reviews it’s about time. Your time. it’s time well spent.

AnnuAl Reviews | Connect with Our experts Tel: 800.523.8635 (us/can) | Tel: 650.493.4400 | Fax: 650.424.0910 | Email: [email protected]

New From Annual Reviews:

Annual Review of Statistics and Its Application Volume 1 • Online January 2014 • http://statistics.annualreviews.org

Editor: Stephen E. Fienberg, Carnegie Mellon University Associate Editors: Nancy Reid, University of Toronto

Stephen M. Stigler, University of Chicago The Annual Review of Statistics and Its Application aims to inform statisticians and quantitative methodologists, as well as all scientists and users of statistics about major methodological advances and the computational tools that allow for their implementation. It will include developments in the field of statistics, including theoretical statistical underpinnings of new methodology, as well as developments in specific application domains such as biostatistics and bioinformatics, economics, machine learning, psychology, sociology, and aspects of the physical sciences.

Complimentary online access to the first volume will be available until January 2015. table of contents: • What Is Statistics? Stephen E. Fienberg • A Systematic Statistical Approach to Evaluating Evidence

from Observational Studies, David Madigan, Paul E. Stang, Jesse A. Berlin, Martijn Schuemie, J. Marc Overhage, Marc A. Suchard, Bill Dumouchel, Abraham G. Hartzema, Patrick B. Ryan

• The Role of Statistics in the Discovery of a Higgs Boson, David A. van Dyk

• Brain Imaging Analysis, F. DuBois Bowman • Statistics and Climate, Peter Guttorp • Climate Simulators and Climate Projections,

Jonathan Rougier, Michael Goldstein • Probabilistic Forecasting, Tilmann Gneiting,

Matthias Katzfuss • Bayesian Computational Tools, Christian P. Robert • Bayesian Computation Via Markov Chain Monte Carlo,

Radu V. Craiu, Jeffrey S. Rosenthal • Build, Compute, Critique, Repeat: Data Analysis with Latent

Variable Models, David M. Blei • Structured Regularizers for High-Dimensional Problems:

Statistical and Computational Issues, Martin J. Wainwright

• High-Dimensional Statistics with a View Toward Applications in Biology, Peter Bühlmann, Markus Kalisch, Lukas Meier

• Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data, Kenneth Lange, Jeanette C. Papp, Janet S. Sinsheimer, Eric M. Sobel

• Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond, Elena A. Erosheva, Ross L. Matsueda, Donatello Telesca

• Event History Analysis, Niels Keiding • Statistical Evaluation of Forensic DNA Profile Evidence,

Christopher D. Steele, David J. Balding • Using League Table Rankings in Public Policy Formation:

Statistical Issues, Harvey Goldstein • Statistical Ecology, Ruth King • Estimating the Number of Species in Microbial Diversity

Studies, John Bunge, Amy Willis, Fiona Walsh • Dynamic Treatment Regimes, Bibhas Chakraborty,

Susan A. Murphy • Statistics and Related Topics in Single-Molecule Biophysics,

Hong Qian, S.C. Kou • Statistics and Quantitative Risk Management for Banking

and Insurance, Paul Embrechts, Marius Hofert

Access this and all other Annual Reviews journals via your institution at www.annualreviews.org.

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