PSY 331 Psychology of Learning WK5-D1

Ch 7: Evolving Frameworks

Evolving Frameworks

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Introduction

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Introduction

Have you ever:

· questioned your own ability to learn effectively?

· been told or believed that we all learn differently?

· experienced moments when learning seemed easier or more difficult to you?

· questioned the validity of intelligence scores?

· believed that an instructor, friend, or family member did not think you were capable of success?

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Understanding how you process information and respond to certain situations is important to understanding how you learn.

If you answered yes to any of these questions, the information in this chapter may help explain why. As mentioned in the Introduction, evolving frameworks are those that continue to be tested, questioned, expanded, and critiqued. Scholars suggest that successful learning is affected by multiple variables such as intellect, emotional development, learning preferences or styles, and whether advances in technology support effective learning (Gardner, 2011a, 2011b; Jonassen, Howland, Marra, & Crismond, 2008; Wicks, Nakisher & Grimm, 2016; Ormrod, 2008; Sternberg, 2015). This chapter discusses the roles of these variables in learning but emphasizes that these variables and their connections with learning are still being researched. By including information about evolving frameworks, this chapter’s discussions aim to support a more holistic understanding of learning and learning effectiveness. Each framework also offers different explanations about what aptitudes are, or are not, related to learning.

Specifically, as outlined in this text, intelligence is defined as the ability to acquire, adapt, understand, and use knowledge. But this definition may differ from person to person. The word intelligence has taken on different meanings as academics explore additional ways to identify, measure, and define it, which you will learn more about in this chapter. Definitions of intelligence have continued to evolve as researchers bring to light new findings and ideas about information processing, knowledge acquisition, and the effects of motivation, aging, emotions, and culture. Some researchers now suggest a more comprehensive view of intelligence by identifying different types of intelligence.

From a physiological point of view, learning involves specific cognitive processes. Most human beings can learn. Without this ability, humanity would not survive. Learning happens whether we learn through basic associations that occur through stimulus-response mechanisms (behaviorism) or through active engagement of our attention to develop knowledge (cognitivism). It is this truth that guides the field of psychology to understand more about learning. When used appropriately, evolving frameworks, such as an awareness of one’s learning style or emotional development and the application of technological tools, can all be used to support and even enhance learning.

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7.1 Intelligence and Intelligence Quotie…

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7.1 Intelligence and Intelligence Quotient (IQ)

Human intelligence has been studied since the early 1900s. But what is intelligence? Is it about knowing the right answers or how to think more abstractly? How do we measure intelligence? French psychologists Alfred Binet and Théodore Simon were two of the earliest to design an intelligence test: the Binet-Simon intelligence scale . The French government was looking for a method that could allow it to more easily identify children with learning disabilities, and the Binet-Simon intelligence scale was the result. (Lewis Terman of Stanford University later adapted this assessment for use in the United States.) The scale was based on experiments that identified the average performance levels of a range of age-based groups, and Binet and Simon created a mathematical formula that could be used to specify an individual’s intelligence quotient (IQ) : mental age (determined by an assessment) divided by chronological age and then multiplied by 100.

Throughout time, intelligence testing has been studied, elaborated upon, and reformulated. Table 7.1 lists and describes a few of the tests that have been utilized to assess intelligence.

Table 7.1: Examples of other intelligence tests

From Yoakum & Yerkes, 1920; ASVAB, 2017; Falik & Feuerstein, 2005; Cattell, 1949; Bilker et al., 2012; Wechsler, 1939; Feuerstein et al., Hadassah WIZO Canada Research Institute, Bar Ilan University Hadassah WIZO Canada Research Institute, Bar Ilan University Hadassah WIZO Canada Research Institute; Department of Psychology, Yale UniversityHadassah WIZO Canada Research Institute, Bar Ilan UniversityHadassah WIZO Canada Research Institute, Bar Ilan UniversityHadassah WIZO Canada Research Institute, Hadassah WIZO Canada Research Institute 1986.

But even with established assessments in the field of learning psychology, the question remains: Does IQ testing effectively support a person’s ability to be a more effective learner? Hilliard (1982) has suggested it does not:

[T]he critical weakness of the use of I.Q. testing in schools is less with the popularly discussed questions of cultural bias and predictive validity than with the simple fact there is not a shred of empirical evidence that the predictive function itself, based on an attempt to do a global ranking of students by intellect serves any valid pedagogical function at all. In other words, there are no data to show that teaching or learning are improved as a consequence of the use of I.Q. tests. . . . Our present system of testing yields no important knowledge of the learning process and no knowledge of the teaching treatment. We know the answers which are given, but do not know what is behind the answers. Therefore, test givers are not in a position to help teachers. (p. 5)

It is important to consider the role that IQ testing has in the bigger picture of how we learn. A better understanding about how these assessments are administered, and the reasoning for using such tools, can help us to further develop our knowledge about learning. The first excerpt in this section is from Sternberg (2015). It provides an overview about intelligence and its definition.

Excerpts from “Human Intelligence”

By R. J. Sternberg

Defining Intelligence

Human intelligence: mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.

Much of the excitement among investigators in the field of intelligence derives from their attempts to determine exactly what intelligence is. Different investigators have emphasized different aspects of intelligence in their definitions. For example, in a 1921 symposium the American psychologists Lewis M. Terman and Edward L. Thorndike differed over the definition of intelligence, Terman stressing the ability to think abstractly and Thorndike emphasizing learning and the ability to give good responses to questions. More recently, however, psychologists have generally agreed that adaptation to the environment is the key to understanding both what intelligence is and what it does. Such adaptation may occur in a variety of settings: A student in school learns the material he needs to know in order to do well in a course; a physician treating a patient with unfamiliar symptoms learns about the underlying disease; or an artist reworks a painting to convey a more coherent impression. For the most part, adaptation involves making a change in oneself in order to cope more effectively with the environment, but it can also mean changing the environment or finding an entirely new one.

Effective adaptation draws upon a number of cognitive processes, such as perception, learning, memory, reasoning, and problem solving. The main emphasis in a definition of intelligence, then, is that it is not a cognitive or mental process per se but rather a selective combination of these processes that is purposively directed toward effective adaptation. Thus, the physician who learns about a new disease adapts by perceiving material on the disease in medical literature, learning what the material contains, remembering the crucial aspects that are needed to treat the patient, and then utilizing reason to solve the problem of applying the information to the needs of the patient. Intelligence, in total, has come to be regarded not as a single ability but as an effective drawing together of many abilities. This has not always been obvious to investigators of the subject, however; indeed, much of the history of the field revolves around arguments regarding the nature and abilities that constitute intelligence.

Source: Sternberg, R. J. (2015). Human intelligence. Encyclopedia Britannica, Inc. Retrieved from https://www.britannica.com/topic/human-intelligence-psychology. Reprinted with permission from Encyclopædia Britannica, © 2017 by Encyclopædia Britannica, Inc.

Intelligence Quotient (IQ)

For centuries, researchers have debated about which constructs should be considered when evaluating intelligence (e.g., perception, learning, memory, reasoning, and problem solving). There are also debates about the variables that could potentially affect intelligence. Questions considered during discussions about intelligence might include the following:

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Though testing is an efficient way to determine intelligence level, there are often questions regarding the variables. Can IQ change with external factors? Does it increase the older we grow?

· Is intelligence fixed, or can it change?

· Is intelligence inherited, or can experiences affect it?

· Are there inconsistencies in definitions of intelligence that make it difficult to accept the possibility that it is a purely inherited characteristic?

· If intelligence is not purely physiological, should other variables be considered when assessing an individual’s potential for success?

· Does intelligence change as we age, or is it simply the slowing of cognitive processing?

· If intelligence does degrade with age, how can we measure whether there is or is not a change?

A critical analysis of intelligence should also consider research that has suggested that IQ scores (e.g., the Binet-Simon intelligence scale) have historically risen throughout the world. This rise in intellect is known as the Flynn effect and is a result of research spearheaded by James Flynn, who found that from year to year, decade to decade, the number of items answered correctly on IQ tests in developed nations is increasing. This research has effectively identified the need to reassess and adjust IQ tests to maintain the average score of 100 (Gottfredson, 2011). If IQ is increasing, does this suggest the probable influence of outside variables on intelligence?

Reinforcing Your Understanding: The Flynn Effect

Did you know that as far as IQ goes, we are getting smarter? Or are we? Much research has suggested that IQ scores are rising (Colom, Flores-Mendoza, & Abad, 2007; Flynn, 2012; Flynn & Rossi-Case, 2012). The following video features James Flynn, whose research on intelligence spans 30 countries. His findings about intelligence have suggested that there has been an increase of intelligence levels over time and that the intelligence quotient (IQ) can be affected by the environment.

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It should be noted that other studies have suggested a reversal Flynn effect, that scores are decreasing (Sundet, Barlaug, & Torjussen, 2004; Dutton & Lynn, 2015). Interested in learning more about the reversal effect? Read “The Negative Flynn Effect: A Systematic Literature Review” by Dutton, van der Linden, and Lynn (2016).

But there are varying definitions of intelligence among researchers. For example, in the scholarly community that studies intelligence, g represents the general factor of intelligence , which considers a broader inclusion of variables (e.g., measurement of life outcomes) than an intelligence quotient (IQ), which is measured by a simple number (e.g., assessed mental age divided by chronological age and then multiplied by 100 [the mean is always 100]). IQ is assessed by a set of standardized tests developed to measure a person’s cognitive abilities (intelligence quotient) in relation to his or her age group. General intelligence, however, is measured through an analysis of several mental assessments tests (Gottfredson, 1997, 1998).

What we believe to be true about intelligence today is that how intelligence is measured should continue to evolve. Numerous researchers in the field of intelligence are adhering to g as a more reliable assessment for success than IQ (Gottfredson, 1997, 1998). According to Myers (2009), “g is one of the most reliable and valid measures in the behavioral domain . . . and it predicts important social outcomes such as educational and occupational levels far better than any other trait” (p. 105).

Additional researchers have suggested that “creativity,” which includes some of the basic abilities measured by g (Bink & Marsh, 2000), is associated with brilliant scientists, mathematicians, musicians, artists, and engineers, and, as such, these individuals do not necessarily score higher on IQ than less creative individuals do (Simonton, 2000). To simplify and help clarify the differences between g and IQ, see Figure 7.1.

Figure 7.1: g versus IQ

General intelligence (g) and intelligence quotient (IQ) consider different factors to assess intelligence.

© Bridgepoint Education, Inc.

It is important to understand that the differences between IQ and g can be somewhat ambiguous. However, in the case of g, one could potentially associate its measurement with that of a student who performs well in one area of math. General intelligence would suggest that this student would also perform well in other mathematical areas. IQ, on the other hand, produces a score that is predictive of one’s level of intellect in comparison with others in the same age bracket.

There are many areas of consideration when learning about intelligence and its applicability to learning. In 1996, Ulrich Neisser and a group of experts published an article that reviewed the available information about intelligence and contained the facts and important questions that experts in the field agreed upon at the time. The following excerpts are from Nisbett et al. (2012). In their article, Nisbett and colleagues aim to offer an updated assessment of intelligence, 15 years after the review published by Neisser et al. Their assessment includes an explanation about different types of suggested intelligences, although they clarify that they do not personally see a great amount of difference between each of the types. Understanding the multiple perspectives on intelligence can be complicated, but the excerpts from Nisbett et al. (2012) will help guide you through some of the more prominent concepts about intelligence to aid in your understanding about the psychology of learning.

Excerpts from “Intelligence: New Findings and Theoretical Developments”

By R. E. Nisbett, J. Aronson, C. Blair, W. Dickens, J. Flynn, D. F. Halpern, and E. Turkheimer

[. . .] Fifteen years after publication of the review by Neisser and colleagues (1996), a great many important new facts about intelligence have been discovered. It is our intent in this review to update the Neisser et al. article (which remains in many ways a good summary of the field of intelligence). There are three chief respects in which our review differs importantly from that of Neisser and colleagues:

1. Due in part to imaging techniques, a great deal is now known about the biology of intelligence.

2. Much more is known about the effects of environment on intelligence, and a great deal of that knowledge points toward assigning a larger role to the environment than did Neisser and colleagues and toward a more optimistic attitude about intervention possibilities.

3. More is now known about the effects of genes on intelligence and on the interaction of genes and the environment.

We also present a wide range of new theoretical questions and review some attempted solutions to those questions. We do not claim to represent the full range of views about intelligence. We do maintain, however, that few of the findings we report have been widely contradicted. [. . .]

Measuring IQ

The measurement of intelligence is one of psychology’s greatest achievements and one of its most controversial. Critics complain that no single test can capture the complexity of human intelligence, all measurement is imperfect, no single measure is completely free from cultural bias, and there is the potential for misuse of scores on tests of intelligence. There is some merit to all these criticisms. But we would counter that the measurement of intelligence—which has been done primarily by IQ tests—has practical value because it is a reasonably good predictor of grades at school, performance at work, and many other aspects of success in life (Gottfredson, 2004; Herrnstein & Murray, 1994). For example, students who score high on tests such as the SAT and the ACT, which correlate highly with IQ measures (Detterman & Daniel, 1989), tend to perform better in school than those who score lower (Coyle & Pillow, 2008). Similarly, people in professional careers, such as attorneys, accountants, and physicians, tend to have high IQs. Even within very narrowly defined jobs and on very narrowly defined tasks, those with higher IQs outperform those with lower IQs on average, with the effects of IQ being largest for those occupations and tasks that are most demanding of cognitive skills (Schmidt & Hunter, 1998, 2004). It is important to remain vigilant for misuse of scores on tests of intelligence or any other psychological assessment and to look for possible biases in any measure, but intelligence test scores remain useful when applied in a thoughtful and transparent manner.

IQ is also important because some group differences are large and predictive of performance in many domains. Much evidence indicates that it would be difficult to overcome racial disadvantage if IQ differences could not be ameliorated. IQ tests help us to track the changes in intelligence of different groups and of entire nations and to measure the impact of interventions intended to improve intelligence.

Types of intelligence other than the analytic kind examined by IQ tests certainly have a reality. Robert Sternberg and his colleagues (Sternberg, 1999, 2006) have studied practical intelligence, which they define as the ability to solve concrete problems in real life that require searching for information not necessarily contained in a problem statement, and for which many solutions are possible, as well as creativity, or the ability to come up with novel solutions to problems and to originate interesting questions. Sternberg and his colleagues maintain that both practical intelligence and creativity can be measured, that they correlate only moderately with analytic intelligence as measured by IQ tests, and that they can predict significant amounts of variance in academic and occupational achievement over and above what can be predicted by IQ measures alone. [. . .]

The chief measure that we focus on review is IQ, because it is for that measure that the bulk of evidence pertinent to intelligence exists. When relevant, we distinguish between IQ and g. [. . .] Many intelligence investigators place great importance on the difference between g and IQ on the grounds that g tends to predict some academic and life outcomes and group differences better than does IQ and because it correlates with some biological measures better than does IQ (Jensen, 1998). We do not in general share the view that g is importantly different from IQ, and we do not interpret correlations between g and life outcomes, group differences, and biological measures as having the same import as do many other investigators. Our differences with those scientists will be noted at several points in this review.

An important distinction commonly made in the literature is between crystallized intelligence, g(C), or the individual’s store of knowledge about the nature of the world and learned operations such as arithmetical ones that can be drawn on in solving problems, and fluid intelligence, g(F), which is the ability to solve novel problems that depend relatively little on stored knowledge as well as the ability to learn. A test that is often considered the best available measure of g(F) is Raven’s Progressive Matrices. This test requires examination of a matrix of geometric figures that differ from one another according to a rule to be identified by the individual being tested. This rule is then used to generate an answer to a question about what new geometrical figure would satisfy the rule. Some of the recent advances in intelligence research, particularly those in the area of the neurobiology of intelligence, have tended to strongly support the distinction between g(F) and g(C) (Blair, 2006; Horn & McArdle, 2007). [. . .]

Genes and the Environment

[. . .] Scientists have learned that not only intelligence but practically every aspect of behavior on which human beings differ is heritable to some extent—meaning that parents pass along certain aspects to their biological offspring. Several strands of evidence, however, suggest that the effects of genes on intelligence, though undeniable, are not nearly as influential as those who promote the role of heritability might have hoped or as environmentalists feared 25 years ago. [. . .]

Studies of Group Differences

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There are claims that intelligence scores vary among different races and socioeconomic classes and that genes could have an effect, too.

One example of the group differences in heritability is the claim that the heritability of intelligence test scores differs as a function of age, with heritability increasing over the course of development (Plomin, DeFries, & McClearn, 1990). Another example of group differences is that the heritability of intelligence test scores is apparently not constant across different races or socioeconomic classes. Sandra Scarr (Scarr-Salapatek, 1971) published a report of twins in the Philadelphia school system showing that the heritability of aptitude and achievement test scores was higher for White children than Black children and for twins raised in relatively richer homes than for twins raised in poorer ones. This report, although it received some positive attention at the time, also faced serious criticism (Eaves & Jinks, 1972). [. . .]

Based on our analyses of several studies, it appears reasonable to conclude that the heritability of cognitive ability is lessened among impoverished children and young adults in the United States. There is mixed evidence as to whether the effect occurs in other countries, and there is contradictory evidence about whether the effect persists into adulthood. One can only speculate about why these differences among studies might occur. It appears, for example, that socioeconomic differences in intelligence are not as pronounced in modern Europe as they are in the United States (e.g., Asbury, Wachs, & Plomin, 2005; Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003; van der Sluis, Willemsen, de Geus, Boomsma, & Posthuma, 2008). [. . .] Studies of adults must contend with the low magnitude of shared environmental components overall, and it may be difficult to detect interactions when there are low shared environment effects to work with.

Possible Explanations

One interpretation of the finding that heritability of IQ is very low for lower socioeconomic status (SES) individuals is that children in poverty do not get to develop their full genetic potential. If true, there is room for interventions with that group to have large effects on IQ. That this interpretation of the finding is correct is indicated by an actual intervention study (Turkheimer, Blair, Sojourner, Protzko, & Horn, 2012). [. . .]

Many have concluded that environments are relatively unimportant in determining IQ, since variations in the environments of adoptive families are not very highly associated with variation in children’s IQ. But work by Stoolmiller (1999) shows that estimates of the relative contributions of genes and environment may be very sensitive to the inclusion of disadvantaged populations in a given study. Adoption studies may tend to underestimate the role of environment and overstate the role of genetics due to the restricted social class range of adoptive homes. Adoptive families are generally of relatively high SES. Moreover, observation of family settings by the HOME technique (Home Observation for Measurement of the Environment; Bradley et al., 1993; Phillips, Brooks-Gunn, Duncan, Klebanov, & Crane, 1998) shows that the environments of adoptive families are much more supportive of intellectual growth than are those of nonadoptive families. The restriction of range (as much as 70% in some studies; Stoolmiller, 1999) means that the possible magnitude of correlations between adoptive parents’ IQ and that of their children is curtailed. [. . .]

New Knowledge About the Effects of the Environment

Much new knowledge about relationships between environmental factors and intelligence has accrued since the Neisser et al. (1996) report appeared, especially regarding the interplay of biological and social factors, which thus has blurred the line between biological and environmental effects on intelligence.

Biological Factors

A wide range of environmental factors of a biological nature influences intelligence. Most of the known factors are detrimental, having to do with a lack of micronutrients and the presence of environmental toxins, and they were reviewed briefly by Neisser et al. (1996). Little of note concerning these effects has been uncovered since then, but there is not much research in this area.

There is, however, one biological factor that seems to increase intelligence, and that occurs early in life. Breastfeeding may increase IQ by as much as 6 points (Anderson, Johnstone, & Remley, 1999; Mortensen, Michaelsen, Sanders, & Reinisch, 2002) for infants born with normal weight and by as much as 8 points for those born prematurely (Anderson et al., 1999; Lucas, Morley, Cole, Lister, & Leeson-Payne, 1992), and the advantage seems to persist into adulthood (Mortensen et al., 2002). One meta-analysis found only a 3-point effect of breastfeeding on IQ when social class and IQ of the mother were controlled (Anderson et al., 1999), and another found essentially no effect on academic achievement scores when the mother’s IQ was controlled except for a modest effect for children breastfed for more than 7 months (Der, Batty, & Deary, 2006).

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Some theorists believe that a parent speaking and interacting positively with a child contributes to a higher IQ in that child. This is an example of how environment may have an effect on intelligence.

It is far from clear, however, that the mother’s social class and IQ is what accounts for the relationship between breastfeeding and IQ. Human breast milk contains fatty acids that are not found in formula and that have been shown to prevent neurological deficits in mice (Catalan et al., 2002). [. . .] The breastfeeding issue remains in doubt. There are some contradictions in the literature up to this point. Nevertheless, the prudent recommendation for new mothers is that, absent any medical conditions, they should breastfeed.

Social Factors

We can be confident that the environmental differences that are associated with social class have a large effect on IQ. We know this because adopted children typically score 12 points or more higher than comparison children (e.g., siblings left with birth parents or children adopted by lower SES parents), and adoption typically moves children from lower to higher SES homes. [. . .]

The evidence from adoption studies that social class greatly affects the IQ of children raises questions about exactly what correlates of SES affect IQ.

Some recent evidence indicates that there are marked differences, beginning in infancy, between the environment of higher SES families and lower SES families in factors that plausibly influence intellectual growth. One of the more important findings about cognitive socialization concerns talking to children. Hart and Risley (1995) showed that the child of professional parents has heard 30 million words by the age of 3, the child of working-class parents has heard 20 million words, and the vocabulary is much richer for the higher SES child. The child of unemployed African American mothers has heard 10 million words by the age of 3. Hart and Risley also found a large difference in the ratio of encouraging comments made to children versus reprimands. The child of professional parents received six encouragements for every reprimand, the child of working-class parents received two encouragements per reprimand, and the child of unemployed African American mothers received two reprimands per encouragement. [. . .]

It should be acknowledged, however, that at present there is no way of knowing how much of the IQ advantage for children with excellent environments is due to the environments per se and how much is due to the genes that parents creating those environments pass along to their children. In addition, some of the IQ advantage of children living in superior environments may be due to the superior genetic endowment of the child producing a specific observable characteristic that rewards the parents for creating excellent environments for intellectual development (Braungart, Plomin, DeFries, & David, 1992; Coon, Fulker, DeFries, & Plomin, 1990; Plomin, Loehlin, & DeFries, 1985). To the extent that such processes play a role, the IQ advantage of children in superior environments might be due to their own superior genes rather than to the superior environments themselves.

It is almost surely the case, however, that a substantial fraction of the IQ advantage is due to the environments independent of the genes associated with them. This is because we know that adoption adds 12–18 points to the IQ of unrelated children, who are usually from lower SES backgrounds. Home environments are not the only candidates for explaining shared environment effects. Home environments are correlated with neighborhood, peer, and school environments. These likely are also important components of the shared environment effects that are reflected in the adoption outcomes for children in families of different social classes. But we stress that we have no direct evidence of the impact of any particular environmental factor on IQ. [. . .]

Sex Differences in Intelligence

Questions about sex differences in intelligence differ from other group differences because there are some fundamental biological differences between males and females and, despite sex-related differences in socialization practices, both sexes share home environments and SES. However, as for questions about racial differences, the topic is incendiary because the answers we provide have political ramifications that include how and whom we educate, hire, and select as leaders.

Some public school districts have begun to segregate girls and boys on the basis of the belief that they are so different intellectually that they need to be educated separately, a belief that stems from faulty extrapolations from research on sex differences in intelligence. An extensive review conducted by the U.S. Department of Education (Mael, Alonso, Gibson, Rogers, & Smith, 2005) found that the majority of studies comparing single-sex with coeducational schooling reported either no difference or mixed results, and other reviews reported a host of negative consequences associated with single-sex education, including increased sex role stereotyping, which may harm both boys and girls (Halpern et al., 2011; Karpiak, Buchanan, Hosey, & Smith, 2007). There are some cognitive areas that show average sex differences, but the data from the research literature on intelligence and cognitive skills do not indicate that different learning environments for females and males would be advisable. [. . .]

Working Memory and Intelligence

Over the last two decades, many researchers have noted that working memory and fluid intelligence, g(F), are highly related concepts. Working memory (see Chapter 3) is the active processing system that simultaneously stores and manipulates relevant information, often in the face of distracting or competing information or the need to inhibit incorrect responses (Engle, 2002). The predominant model of working memory posits verbal, visuo-spatial, and episodic memory as three subsystems that are coordinated by an executive that is often conceptualized as attentional control (Baddeley, 2002; Baddeley & Hitch, 1974). The capacity of working memory is measured with tests that require participants to keep verbal or visuo-spatial information in memory while solving problems such as sentence comprehension, arithmetic, and abstract reasoning. Scores on tests of working memory capacity have high correlations with reading comprehension, reasoning abilities, and scores on the SAT (Daneman & Carpenter, 1980; Kyllonen & Christal, 1990; Turner & Engle, 1989). [. . .]

The argument about whether working memory explains individual differences in g(F) is important because of the close association of g(F) with g. Following from this association, scientists have recently shown that g(F) can be markedly enhanced by training people to increase the capacity of their working memory. However, the research establishing this fact provides no indication that the training of these functions influences g(C). Thus the case for identifying either working memory or g(F) with g is rendered implausible. Nevertheless, the relations among working memory and other executive functions, g(F), and g are clearly complex, and much remains to be learned. [. . .]

Stress, Intelligence, and Social Class

[. . .] Chronic, continuous stress—what can be considered as “toxic” stress—is injurious over time to organ systems, including the brain. Chronically high levels of stress hormones damage specific areas of the brain that are important for regulating attention and for short-term memory, long-term memory, and working memory (McEwen, 2000). Although the extent to which the effect of early stress on brain development and stress physiology may affect the development of intelligence is not currently known, we do know that (a) stress is greater in low-income home environments (Evans, 2004) and (b) a low level of stress is important for self-regulation and early learning in school (Blair & Razza, 2007; Ferrer & McArdle, 2004; Ferrer et al., 2007).

Research suggests that part of the Black–White IQ gap may be attributable to the fact that Blacks, on average, tend to live in more stressful environments than do Whites. This is particularly the case in urban environments, where Black children are exposed to multiple stressors (e.g., Sharkey, 2010). [. . .] An impressive study by Eccleston (2011) indicates that even stress on the pregnant mother may have enduring effects on her children. The children born to women in New York City who were in the first six months of pregnancy when 9/11 occurred had lower birth weights than children born before 9/11 or well after it, and the boys at the age of 6 were more than 7% more likely to be in special education and more than 15% more likely to be in kindergarten rather than first grade. Oddly, girls’ academic status was unaffected by mothers’ stress. Investigation of relations between early stress and intelligence thus seems an important direction for future research. A particularly important issue concerns the degree to which the effects of stress on the brain are reversible.

These unresolved issues are merely examples of some of the important contemporary paradoxes and unknowns in intelligence research. It is to be hoped that as much progress on these and other issues will be made in the next 15 years as has been made on some of the paradoxes and unknowns since the time of the Neisser et al. (1996) review.

Source: Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159. Copyright © 2012, American Psychological Association. Reproduced with permission.

Nisbett and colleagues (2012) offer a well-rounded explanation of intelligence quotient (IQ), including the multifaceted variables that different research has suggested may affect IQ. Intelligence is complex, and the range of notions about it makes it a controversial topic among researchers. Thus, an exploration of less numerically derived measurements used to explain what intelligence may or may not be is also an important area of discussion.

The next two sections will explore some alternate views of intelligence, offering a better understanding of the role of intelligence and how it affects learning. Though the discussions explore areas that some psychologists might consider less traditional (multiple intelligences and emotional intelligence), an evaluation of these areas is crucial to developing a holistic understanding of learning.

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7.2 Multiple Intelligences (MI)

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7.2 Multiple Intelligences (MI)

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The idea of multiple intelligences is that one person could have different abilities and talents than someone else and, thus, has different intelligences. For example, a person with exceptional skill in learning and playing different instruments could claim that he or she has musical intelligence.

New intelligence models, with an increased recognition of culturally inclusive implications, have been introduced, driven much by critics of standard intelligence testing and calls for change (Larin et al., 2011). An example of this all-inclusive focus is found in Howard Gardner’s perspective on intelligence. Gardner’s model of multiple intelligences (MI) suggests that individual learning varies from person to person based on the biopsychological and cultural factors, such as genetics, mood, personality, and socioeconomics, that can affect skill development. In general, the core notion is that humans possess differing abilities, and the model illustrates these ability areas as “intelligences.” Further, Gardner clarifies how the MI model differs from the “learning styles” that have been proposed by other researchers (and will be discussed in section 7.4):

The concept of style designates a general approach that an individual can apply equally to every conceivable content. In contrast, an intelligence is a capacity, with its component processes, that is geared to a specific content in the world (such as musical sounds or spatial patterns). (Gardner, 1995, pp. 202–203)

Critics of MI have suggested that although humans may have increased abilities in specific areas, the idea that these should be considered intelligences is academically unproven and irresponsible, and further confuses our understanding of intelligence (McGreal, 2013; Sternberg, 1985).

Some of the inconsistencies among researchers’ views of MI are evident in the following quotations:

· Ormrod (2006) defined intelligence as “the ability to modify and adjust one’s behaviors in order to accomplish new tasks successfully. It involves many different mental processes and may vary in nature depending on one’s culture” (p. 140).

· Sternberg (2015) noted that human intelligence is a “mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment” (para. 1).

· In contrast, Gardner defined intelligence as “a biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture” (as cited in Willingham, 2004, p. 19).

· In response to MI, Sternberg stated that “multiple intelligences might be better referred to as multiple talents” (Sternberg, 1985, p. 1114).

The series of excerpts in this section is from McFarlane (2011). The excerpts introduce MI as an accepted theory of intelligence, and thus you should continue to apply skeptical inquiry as you evaluate the information. An understanding of MI can help supplement your learning knowledge base, but it should not replace the other perspectives presented in this text. McFarlane (2011) describes the nine types of intelligence identified by Gardner, explains why Gardner’s approach to intelligence is effective for learning environments, and highlights concerns expressed by other researchers about Gardner’s theory of MI.

Excerpts from “Multiple Intelligences: The Most Effective Platform for Global 21st Century Educational and Instructional Methodologies”

By D. A. McFarlane

Nine Types of Intelligence

Theories of intelligence are extensive in educational and psychological literature. However, regardless of the number of theories and perspectives that have emerged on intelligence, each with varying combinations of the original nature-nurture argument that underpins so many debates in social life, intelligence as a subject of education seems to have no exhaustion point. From the conception of a singular intelligence to Gardner’s multiple intelligences, each theory or perspective has significantly affected educational and instructional methodologies. Multiple intelligences (MI) consist of many subdivisions of individual abilities and potential according to their learning modes. Gardner (2011a) believes that there are nine distinctive types of intelligences: linguistic intelligence, logical-mathematical intelligence, musical-rhythmic intelligence, bodily-kinesthetic intelligence, spatial intelligence, naturalist intelligence, interpersonal intelligence, intrapersonal intelligence, and existential intelligence. Smith (2008) provides brief definitions of the first seven of the nine intelligences developed by Gardner in Table 7.2, and to which have been added the other two types of intelligences.

Table 7.2: Gardner’s multiple intelligences

From “Howard Gardner, Multiple Intelligences and Education,” by M. K. Smith, 2008 (http://www.infed.org/thinkers/gardner.htm). Copyright 2008 by M. K. Smith. Reprinted with permission.

According to the originator of MI theory, Howard Gardner, intelligence can be defined in three ways, as (i) a property of all human beings, (ii) a dimension on which human beings differ, and (iii) the ways in which one carries out a task in virtue of one’s goals (Gardner, 2011a, p. ix). Gardner believes that the solid basis for MI theory lies in biopsychological potentials that range across cultural contexts. As such, the theory of multiple intelligences decisively addresses the deficiencies of many theories that do not take individual differences into deep consideration as the basis of intelligence, but rather focus on intelligence as a consensus-driven concept such that standardized tests have always been the norm for measuring intelligence. However, Gardner and Hatch (1989) note that standard intelligence tests are incapable of tapping into the expanse of human potential that we call intelligence.

Gardner and Hatch’s view on intelligence is further supported to some great degree by other scholars. Two notable examples are Robert J. Sternberg and Daniel Goleman. This is the case because both theorists differ significantly “like Gardner” when it comes to the traditional or what could be called the “cognitive” view of intelligence. The very definitions of intelligence provided by both these theorists reflect the same detachment from traditionalist theories of intelligence that is espoused by Gardner. Sternberg (2004) defines intelligence as “skill in achieving whatever it is you want to attain in your life within your sociocultural context by capitalizing on your strengths and compensating for, or correcting, your weaknesses” (p. 1). Goleman (1995) differs only slightly by having a more “singular” definition of intelligence: emotion. He defines intelligence as “abilities such as being able to motivate oneself and persist in the face of frustrations; to control impulse and delay gratification; to regulate one’s moods and keep distress from swamping the ability to think; to empathize, and to hope” (p. 285). Goleman calls this “emotional intelligence (EI)” and believes that it accounts for 80% of success in individuals.

MI: An Effective Platform

As developed by Dr. Howard Gardner, MI is based on the understanding that people learn utilizing different types of intelligences (Griggs et al., 2009). This means that individual learning varies across a platform of human potentialities in which individual differences stemming from biopsychological and cultural factors affect their skill sets and even abilities. Among intelligence theories, MI specifically caters to the diversity characterizing individuals, and hence leads to a more effective and sensible approach to address unique learners in the classroom. The implications for educators and students are tremendous in terms of the richness and flexibility MI brings to teaching and learning:

As educators develop and utilize pedagogies that consciously attempt to engage students in a variety of ways, knowing which intelligences students possess is critical to effective instruction. The benefit of this evaluation is two-fold. If instructors know the strengths of their students, they can better prepare engaging and relevant lessons that correlate with those strengths. Secondly, students, knowing their strengths, can engage various strategies to enhance their learning accordingly. (Griggs et al., 2009, p. 55)

MI when compared with other theories of minds or human potential is no doubt the most effective platform upon which to develop educational and instructional methodologies for the classroom of the 21st century. We are living in a truly global society where diversity has become the most defining aspect of social life. This diversity is reflected in the 21st-century school and classroom where students from all walks of life (representing diverse languages, cultures, ethnicities, nationalities, religions, and socialization backgrounds—not to mention unique individual personalities) meet in a single place where the instructor must be able to facilitate vast differences.

Only MI holds the power and potential for instructors or educators to develop flexible and broad enough methodologies and approaches to address this diverse audience with differing skill sets or potentials. This is supported by Haley (2004), who explored the application and suitability of MI in shaping and informing instructional strategies, curricula development, and alternative forms of assessments across second language learners. Second language learners more than many other groups in the classroom represent the extensive diversity that characterizes today’s classrooms and schools, and the application of MI as reported by Haley (2004) attests to the power of Gardner’s theory as the most applicable and effective platform for 21st-century educational and instructional methodologies. Students learn differently, and there is no doubt about that. Some students are visual learners, while others are kinesthetic learners, and yet others a combination of several learning modes based on their individual intelligences. This requires educators to vary pedagogy to effectively reach their students and meet accountability standards (Griggs et al., 2009). In their study attesting to the variability of students’ intelligences as the rationale for educators varying pedagogy based on the existence of multiple intelligences, Griggs et al. (2009) found that, among 167 students across different disciplines,

the intelligences listed most often were self and social, both in the high 60 percents, followed by body movement at 47.2%. Nature, musical, and language followed all with percents in the 20s. The lowest two intelligences listed were logic/mathematical and spatial, both at 17+%. (p. 59)

This is sufficient evidence for multiple intelligences being the most effective platform for instructions across such cohorts. [. . .]

When we examine the ideas of creative, analytical, and practical intelligences (the triarchic intelligence theory), we can see where these present themselves as potentially encompassing all the nine intelligences described by Gardner. (See Figure 7.2.) This stems from the idea that “creativity” broadly defined can encompass any of the nine types of intelligences communicated by Gardner. Practical intelligence also envelops around several intelligences in MI theory, especially those that appear to be more mechanical than intellectual or abstract. Thus, Sternberg’s idea of intelligence represents almost a “contraction” of what Gardner boldly puts out there to allow for flexible considerations in our understandings and definitions of human abilities.

Figure 7.2: Triarchic intelligence theory

The triarchic intelligence theory, like the multiple intelligences model, suggests a more encompassing definition of intelligence that the definition of IQ does not address. This theory suggests there are three types of intellectual abilities: analytical, creative, and practical.

© Bridgepoint Education, Inc.

Further evidence of the formidability of multiple intelligences as the most suitable and effective platform for 21st-century instructional and educational methodologies can be gleaned from the application of various technologies in the learning process. According to Kezar (2001), multiple intelligences (MI) theory allows us to understand the effective application and usage of technology in serving diverse students and in meeting the standards set by various stakeholders, especially as increased accountability in 21st-century education demands that each and every student becomes the focus of teaching. Additionally, multiple intelligences (MI) provides a new lens through which to see and address the problems that have plagued educators, learners, and schools for decades. As Silverstein (1999) notes, “Traditional IQ tests, developed in the early 1900s, deal mainly with logical/mathematical and linguistic intelligences, but those tests are not designed to measure the other kinds of intelligences that people possess” (p. 18). However, MI provides a remedy for this by allowing us to recognize different abilities and capabilities in our children and in people in general. This means that schools are able to expand their curricula and develop better assessments that are more applicable to individual lives and survival needs.

The emergence of emotional intelligence (EI) has also brought new understanding of intelligence that makes Gardner’s MI theory more formidable because the idea of “emotion” allows for even greater relativity and subjectivity in the definition of what truly constitutes intelligence. (See section 7.3 for more about EI.) [. . .]

Criticisms of MI

Regardless of the rich evidence and argument put forward for multiple intelligences (MI) as the most effective platform for 21st-century educational and instructional methodologies, not everyone agrees. One researcher views the MI theory applied to educational methodology as creating negative stereotypes and categorical limitations on learners. According to Lacey (2006), the theory of multiple intelligences, while it holds some importance and great potential, reinforces some negative and fairly limiting stereotypes that affect individual learning and perceptions. Lacey (2006) argues,

Students do tend to think about themselves as being one specific type of learner and they will often dismiss previously untried activities “because it’s just not me.” In the short term, this is counter-productive because education should be about developing a range of abilities, whether you demonstrate an initial aptitude or not. In the long term, it is even worse. People with PhDs are widely assumed to be incapable of tying their own shoelaces because of the belief that high academic ability equals spatial incompetence. (p. 3)

This is a fairly reasonable argument. However, Lacey overlooks the numerous opportunities that MI has created for educators, learners, and educational institutions, and society overall. Because of the development of the perspective of MI, we have acquired a better understanding of how people learn and have been able to facilitate learner differences much more effectively than at any other time in history. In addition, because of the ideas and application of MI, we have been able to eliminate the many barriers that affected learning opportunities for women and minorities of all classes, disabled children, and those who do not display the norm-based cognitive skills that standardized tests were originally formulated around.

Kezar (2001) points out that it is through MI that we are able to respond effectively to increased access in education and teaching and learning, meet the needs of diverse technology users, and respond to accountability demands from various education stakeholders in 21st-century society and schools. MI affords us the opportunities to better understand people from different social, cultural, political, and historical backgrounds and relate to the contexts in which they live and learn. Armstrong (2011) finds multiple intelligences extremely integral to the teaching-learning process in any environment because, he argues, whatever we teach and learn can be connected to the different intelligences, as seen in Table 7.3.

Table 7.3: MI in the teaching-learning rectangle

Note: Though not included in the teaching-learning rectangle, an example method of delivery that might support existential intelligence could be exposure to different philosophies or ideas about the meaning of life.

From “Multiple Intelligences,” by T. Armstrong, 2011 (http://www.institute4learning.com/resources/articles/multiple-intelligences/). Copyright 2011 by T. Armstrong. Reprinted with permission.

With such a broad spectrum, MI clearly addresses the varied aspects of human abilities and skills, since we have all of these intelligences in varying degrees and it is our development along these nine factors or intelligent quotients that makes us fully human, fully functional members of groups, institutions, and society. Teachers and other educators, whether in formal, nonformal, or informal educational settings, teach us through a combination of these “mediums,” which Gardner calls “intelligences.” These intelligences are not only representative of faculties, but parallel the different kinds of methodologies and approaches that we develop in teaching and learning. Even our very theories of learning and ideas about knowledge must fall within categories that encompass these intelligences defined by Gardner. Thus, MI allows for the development of diverse approaches to teaching and learning by allowing educators to develop multiple platforms and examples to bring across experiences to learners. Learning is generally defined as a permanent change in behavior resulting from experience, and nothing better facilitates the development of this experience than ideas rooted in different modes that match individual preferences and abilities. [. . .]

Source: McFarlane, D. A. (2011). Multiple intelligences: The most effective platform for global 21st century educational and instructional methodologies. College Quarterly, 14(2). Retrieved from http://collegequarterly.ca/2011-vol14-num02-spring/mcfarlane.html

Gardner’s model of multiple intelligences (MI) offers learning theorists an alternate way to explain how educators, mentors, and others can support learners and their abilities to effectively acquire new knowledge. Although the model is controversial, it is plausible that each learner could have success in different areas. The investigation into why these differences occur will continue to be an area of interest to scholars and academics as achievement gaps continue to plague classrooms across the country (National Center for Education Statistics, 2001, 2015).

In addition, learning opportunities in the United States are rising as more Department of Education funds go toward programs that help families afford college, support schools in low-income communities, and support students with special needs (U.S. Department of Education, 2017a, 2017b). Increases in such opportunities mean that it is important for instructors, communities, programs, and other supporting organizations to understand the complexities of educating diverse learners, so that learners can meet their goals and achieve success.

Reinforcing Your Understanding: Overview of Gardner’s Theory

Gardner’s theory of multiple intelligences emphasizes the diverse strengths and capacities of learners. Watch the following video for a visual overview of Gardner’s theory and its implications for educational systems. According to this theory, learners, through increased awareness, can use their different intelligences to carry out different tasks and solve problems. The video suggests that learning environments might be more beneficial for learners if information is presented and learning is assessed in multiple ways. However, with an increased understanding, the learner is also able to self-apply strategies to support his or her own learning.

https://howardgardner.com/2017/01/06/mi-theory-overview-video/

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7.3 Emotional Intelligence (EI)

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7.3 Emotional Intelligence (EI)

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According to some researchers, improving emotional intelligence can enable people to learn more effectively (as they are more aware of their own feelings and thought processes) and interact better with others.

Emotional intelligence (EI) is another area that research has suggested can affect our ability to effectively learn and develop. EI is the ability to understand and manage one’s own emotions and the emotions of others. Individuals who develop an awareness of emotions can use this understanding to help guide their own behaviors in a more effective and positive manner (Goleman, 1995). Developing EI can also potentially support individuals’ personal strengths and allow them to manage life in a way that helps them “to passionately fulfill . . . professional and personal goals. . . . Understanding others will help [them] to be more effective in gaining collaboration, as well as influencing individuals, . . . organizations, and communities” (Rosser & Massey, 2014, p. 30). (The embedded video located before the excerpts provides an introductory overview of emotional intelligence.)

Studies that examine the roles of EI in leadership development (Rosser & Massey, 2014; Wicks et al., 2016) and supervision or management (Wicks et al., 2016) are common in this area of research. EI is considered in this text because this different aspect of development could affect one’s efforts to effectively learn new information or skills or one’s ability to encourage effective learning when assisting others. The first series of excerpts in this section is from Wicks, Nakisher, and Grimm (2016). The content explains EI and some of its roles.

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Excerpts from “Emotional Intelligence”

By J. Wicks, S. Nakisher, and L. Grimm

Defining EI

The concept of emotional intelligence is relatively new to the field of psychology. The ideas and concepts that are now referred to as emotional intelligence first came to be in the 1980s, when Howard E. Gardner first proposed his theory of multiple intelligences. The term emotional intelligence was introduced by Peter Salovey and John Mayer in a 1990 research paper. In 1995, the publication of Daniel Goleman’s Emotional Intelligence: Why It Can Matter More Than IQ popularized the concept. [. . .]

A standardized definition of emotional intelligence has yet to emerge. Although not agreed on by researchers in the field, two common definitions of emotional intelligence are the ability to monitor the feelings and emotions of the self and of others and to use this information to guide one’s behaviors, and the ability to identify and control emotions in oneself and in others. [. . .]

Since its inception, the concept of emotional intelligence has been used in a wide variety of contexts to help people live more successfully. Although some of the first contexts in which emotional intelligence was used focused on worker productivity and satisfaction, the concept has since been applied successfully in a broad range of areas.

One of the areas in which emotional intelligence has proven to be very helpful is in relationship training. Emotional intelligence, by its nature, has a strong focus on empathy and on understanding the ways in which emotions influence people. When people are able to combine a sense of how their own emotions can guide or derail them with a developed awareness of how others feel, they are equipped to navigate the complexities of relationships across many situations. Emotional intelligence has also been applied in structuring educational settings for students, teachers, and parents. Curricula have been designed that incorporate appropriate emotional modeling, helping children regulate their emotions and connecting emotional experience to learning. Training children in the classroom in social and emotional skills has been shown to increase academic performance (Salovey & Mayer, 1990).

Emotional intelligence has also been widely used in skills training for supervisors and managers. [. . .] In fact, emotional intelligence and the possession of certain traits has become as important as intellect in the hiring process. Emotional intelligence in the workplace not only promotes better work habits, but it can also help create an environment conducive to productivity.

Source: Wicks, J., Nakisher, S., & Grimm, L. (2016). Emotional intelligence (EI). Salem Press Encyclopedia of Health. Copyright © EBSCO.

Emotional Competencies

Daniel Goleman’s work helped popularize the concept of EI. He has noted that EI is the integration of key components to one’s behavior and thoughts: self-awareness, self-management, social awareness, and relationship management (Goleman, 1995). Unlike other areas of intelligence, Goleman also has suggested that EI can be developed and learned. This consideration, although criticized by some, offers an interesting dynamic to the ability for each of us to attain more success through increased awareness and self-regulation.

The second series of excerpts in this section is from Zeidner, Matthews, and Roberts (2009). The content introduces Goleman’s background and ideas about EI that suggest that the different attributes of EI can be classified as a combination of recognition, regulation, self, and others.

Excerpts from “What We Know About Emotional Intelligence: How It Affects Learning, Work, Relationships, and Our Mental Health”

By M. Zeidner, G. Matthews, and R. D. Roberts

[. . .] Having obtained a PhD from Harvard University, Daniel Goleman became a journalist at the New York Times. During his 12 years there, he worked on various stories relating to the brain and emotion. After reading a scientific article by Mayer and Salovey, he was inspired to write a book that would become one of the best-selling psychological texts ever: Emotional Intelligence: Why It Can Matter More Than IQ. In the book, Goleman (1995) sets out a comprehensive account of EI and its relevance to society. His central thesis is that emotional illiteracy is responsible for many social evils including mental illness, crime, and educational failure. Furthermore, people at work often fall short of their potential through failing to manage their emotions appropriately. Job satisfaction and productivity are threatened by unnecessary conflicts with coworkers, failure to assert one’s legitimate needs, and failure to communicate one’s feelings to others. Goleman pushes the intelligence envelope in various respects throughout his writings. Some of the ways in which his thesis conflicts with conventional psychology are as follows:

Definition of intelligence—Goleman includes qualities such as optimism, self-control, and moral character as part of intelligence. Normally, such qualities are seen as reflecting components of personality, not ability.

Stability of intelligence—Typically, cognitive intelligence has been viewed as fairly stable over time. By contrast, Goleman emphasizes that emotional intelligence can be learned and increased, seemingly at any time, over one’s life span.

Intelligence in everyday life—In order to enjoy a successful life, Goleman (1995, 1998) claims, emotional intelligence is more important than IQ. These success factors include such disparate indicators as being promoted at work and maintaining secure and fruitful relationships with others. Indeed, a subtext of Goleman’s (1995) book is that IQ is much overrated; as one of the chapter headings reads, “Smart is dumb.”

Intelligence with a moral dimension—Conventionally, intelligence refers to a set of capabilities and skills that are equally at the service of the philanthropist and the evil genius. Goleman (1995), however, relates EI to moral character: “Emotional literacy goes hand in hand with education for character, for moral development, and for citizenship” (p. 286).

So what exactly did Goleman (1995) mean by “emotional intelligence”? His first book set out a laundry list of desirable qualities, including self-confidence, sensitivity, self-awareness, self-control, empathy, optimism, and social skills. Indeed, the present authors (Matthews, Zeidner, & Roberts, 2002) criticized Goleman for listing almost every positive quality that was not actually cognitive intelligence. Subsequently, Goleman (2001) sought to put the traits that focally define EI on a more systematic basis. This basic schema is reproduced in Table 7.4.

Table 7.4: Goleman’s (2001) 2-by-2 model of emotional competencies

Zeidner, et al. © 2009 Massachusetts Institute of Technology, by permission of The MIT Press.

Goleman’s model suggests two key divisions separating different aspects of EI. First are distinguished those elements of EI that refer to personal competencies (e.g., self-awareness) from those that relate to social competencies (e.g., empathy). This distinction corresponds to Gardner’s (1983) intrapersonal and interpersonal competencies. Second are distinguished facets of EI that relate to awareness from those that concern the management and regulation of emotion. For example, recognizing that someone is unhappy is different from being able to cheer the person up. And yet both “reading” emotions and changing emotions constructively relate to the overall facility of EI. Combining the division of “self” compared with “others” and “recognition” compared with “regulation” yields the 2-by-2 classification for emotional competencies given in Table 7.4. Each of the various attributes of EI can be classified as belonging to one of the four cells of the table.

Jacoblund/iStock/Thinkstock

According to Goleman, having high emotional intelligence aids in workplace relationships, such as that of manager to employee.

Goleman (2001) argues that the qualities listed are emotional competencies. As such, they may be defined as learned capabilities based on emotional intelligence that result in outstanding performance at work or in other domains of life (see also Goleman, 1998). Leaving aside the circularity of defining emotional competence in terms of emotional intelligence, the definition here emphasizes the dependence of emotional intelligence on learning. By contrast, psychological theories of intelligence have typically defined mental ability in terms of aptitude, that is, a preexisting capacity to acquire specific mental skills through learning. Thus, IQ test scores are normally seen as indicators of the person’s potential for acquiring academic knowledge and not the knowledge itself (Jensen, 1998).

In contrast, Goleman (1998) sees EI as a set of learned skills that may translate directly into success in various social domains, such as the workplace. For example, “the empathy competence” helps team leaders to understand the feelings of team members, leading to greater team effectiveness. This same competence helps the salesperson to close more sales by being better able to “read” the customer’s emotional reactions to a given product. Conversely, emotionally unintelligent behaviors may be highly damaging to organizations. While ostensibly such an argument may be persuasive, more often it is the opposite: As Hogan and Stokes (2006) note, “The primary reason employees leave a company is poor management—people don’t quit organizations, they quit managers” (p. 269). [. . .]

Source: Zeidner, Moshe, Gerald Matthews, and Richard D. Roberts, What We Know About Emotional Intelligence: How It Affects Learning, Work, Relationships, and Our Mental Health, p. 835 word excerpt and 1 Table, © 2009 Massachusetts Institute of Technology, by permission of The MIT Press.

Emotional intelligence (EI) studies have suggested that performance (e.g., organizational [Srivastava, 2013], occupational [Bar-On, Handley, & Fund, 2006], leadership [Rosser & Massey, 2014; Srivastava, 2013], and academic [Kvapil, 2007; Malik & Shahid, 2016]) increases as one’s EI develops (Goleman, 1998). The four constructs of EI, self-awareness, self-management, other awareness, and other management, are all crucial components one can develop (Goleman, 1998; Rosser & Massey, 2014). Learners should be aware of their development in these areas and strengthen these skills to support performance and efficient learning. Self-development such as seeking professional and personal opportunities for growth, learning to control your emotions and respond to others, practicing empathy, and responding to others thoughtfully with their feelings and beliefs in mind are all examples of how EI can be practiced (Rosser & Massey, 2014).

An additional area of self-development that can support effective learning and performance is an awareness of one’s learning style (LS) (Cassidy, 2004). In section 7.4, Cassidy (2004) will elaborate on what, in the context of learning, learning styles are and how they are often measured in research. Also discussed is a comprehensive summary of applications of how learning styles interact with our contexts and support learning and performance.

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Applying Skeptical Inquiry: Knowing Thyself

We have discussed several ideas about what intelligence is and is not. Take the following quizzes and then consider the questions provided.

· Multiple intelligences (http://www.edutopia.org/multiple-intelligences -assessment): This unofficial self-assessment uses what is known about MI, which suggests that individuals have preferred learning approaches, to identify your preferences.

· Emotional intelligence (http://www.ihhp.com/free-eq-quiz/): This unofficial self-assessment uses what is known about EI—which suggests that learning is affected by our ability to perceive, control, and evaluate emotions—to identify your preferences.

Interested in learning more about characteristic assessments? There are many online opportunities (both for free and for a fee) that can help you identify your preferred learning styles. (Or perhaps you’ve taken a learning styles assessment in a previous course?) Just remember to be a skeptical thinker and be wary of websites that are not scholarly in nature, because some assessments may not be reliable (or valid), providing you with inaccurate results.

Questions

1. Do you think the results accurately represent your personal characteristics? Your preferences for learning?

2. How do you think your results, and being more aware of your needs, could help you effectively increase your learning?

3. If the assessment is inaccurate (unreliable or not valid), how might this negatively affect your ability to be effectively self-aware? In what ways could you assess your own learning preferences (e.g., making mental or written notes of the strategies used when you have increased success)?

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7.4 Learning Styles

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7.4 Learning Styles

Learning styles (LS) are specific combinations or patterns of learning activities used during the learning process. As noted in section 7.2, Gardner (2011a) has suggested that an LS differs from an MI, although others might consider the concepts to be synonymous. However, both perspectives agree that human beings have preferred ways of learning, and by better understanding these preferences, learners can be more equipped to adapt to learning conditions. In addition, educators who have an understanding of learning styles could use the information to design effective learning opportunities. Research also has suggested that when learning styles are utilized more effectually (by the instructor and learner), learners are more confident, are more willing to engage, study more effectively, and find the learning process more enjoyable, increasing their likeliness to succeed (Brannan, White, & Long, 2016; Dryden & Voss, 2009). According to Ormrod (2008), some students simply experience more successful learning when given information as pictures rather than words, or vice versa.

The series of excerpts in this section is from Cassidy (2004) and supports the notion that one’s learning styles can influence how effectively one learns. The content describes terms and methods associated with the assessment of learning styles (or learning preferences), providing information about what assessments are available and how they are applied.

Excerpts from “Learning Styles: An Overview of Theories, Models, and Measures”

By S. Cassidy

Terminology and Fundamental Issues

Defining the key terms in this area is not a straightforward task. The terms learning style, cognitive style, and learning strategy are—understandably—frequently used imprecisely in theoretical and empirical accounts of the topic. The terms learning style and cognitive style are, on some occasions, used interchangeably, while at other times they are afforded separate and distinct definitions. Cognitive style is described by Allport (1937) as an individual’s typical or habitual mode of problem solving, thinking, perceiving, and remembering, while the term learning style is adopted to reflect a concern with the application of cognitive style in a learning situation (Riding & Cheema, 1991). Riding and Cheema (1991) go on to describe cognitive style in terms of a bipolar dimension (wholist-analytic), while learning style is seen as encompassing a number of components that are not mutually exclusive. It is also likely that cognitive style—at the very least—can be regarded as one significant component of learning style. Hartley (1998) provides the following definitions: Cognitive styles are the ways in which different individuals characteristically approach different cognitive tasks; learning styles are the ways in which individuals characteristically approach different learning tasks. A third key term in the area, learning strategies, Hartley (1998) defines as the strategies students adopt when studying. Hartley (1998) continues: “Different strategies can be selected by learners to deal with different tasks. Learning styles might be more automatic than learning strategies which are optional” (p. 149). This final point, which attempts to distinguish between style and strategy, reflects a recurring issue in the area.

Jacob Ammentorp Lund/iStock/Thinkstock

Extensive note-taking and keen attention to complex details is an example of a sharpener learning style. Determining and using one’s learning style could help a person be more engaged in and develop effective study habits for what he or she is learning.

The “state-or-trait” debate associated with so many human psychological characteristics (such as personality) is, not surprisingly, relevant here. Learning style may be considered as stable over time (structural)—a trait—or as changing with each experience or situation (process)—a state. Perhaps the more workable view is that a style may well exist in some form, that is, it may have structure, but that the structure is, to some degree, responsive to experiences and the demands of the situation (process) to allow change and to enable adaptive behavior. The “motherboard/software” and “hard/soft” wiring analogies have also been used to describe the interface of style (motherboard/hard wiring) and strategy (software/soft wiring). Investigating the issue of stability in learning style, Loo (1997) did find evidence to support consistency in learning style over time but was critical of current techniques of analysis and recommended caution in drawing any firm conclusion regarding stability.

One final term worthy of definition here is preferences. A number of authors refer to the favoring of one method of learning over another (such as group work over independent study) as learning preferences. [. . .]

Models and Measures

The following list of learning style models and instruments is—as is frequently the case—by no means exhaustive. It is, however, fairly comprehensive and includes descriptions of most of the models at least referred to in recent and significant review papers (De Bello, 1990; Riding & Cheema, 1991; Rayner & Riding, 1997). The selection process certainly did not center on identifying models that differed from each other in such a way as to provide alternative perspectives. Rather, the aim is to make a point of reported overlaps between different models in order to make explicit the need for rationalization in research and practice and encourage readers to identify further similarities. While it would, conceivably, be possible to compile an exhaustive list of instruments, this would probably include many derivatives and adaptations along with a number of instruments without an empirical base and an absence of reliability and validity data. An overview of the models and measures is provided in Table 7.5. [. . .]

Table 7.5: Overview of learning style models and measures

Adapted from “Learning Styles: An Overview of Theories, Models, and Measures,” by S. Cassidy, 2004, Educational Psychology, 24(4), pp. 419–444.

Learning Styles in Action—Some Examples

Interest in defining, characterizing, and studying the associated effects of learning style results—mainly—from its distinction from ability and its association with performance. Whereas the relationship between ability and performance is relatively straightforward, such that performance improves with increased ability, the effects of style on performance are contingent on the nature of the task. For example, imagers are likely to perform better on pictorially based tasks than on verbal-based tasks (Riding, 1997). In support of the independence of learning style and intelligence, Riding and Pearson (1994) found that there were no significant correlations between intelligence—as measured by the British Abilities Scale—and the wholist-analytic and verbal-imager dimensions of learning style. A less clear distinction between learning style and personality is presented (Riding & Wigley, 1997), although only a tentative link is reported. The identification of an individual characteristic, separate from ability, that has impacts on learning performance has led to the application of learning style theory and measurement in a number of diverse areas. [. . .] These examples illustrate the range of potential applications of learning style and underline the need to promote clarification and rationalization in the field.

Academic Achievement

Cassidy and Eachus (2000) used the Approaches and Study Skills Inventory for Students (Tait & Entwistle, 1996) to measure learning style in undergraduate students. They found that academic achievement was positively correlated with a strategic approach, negatively correlated with an apathetic approach, and unrelated to a deep approach to learning. Learning style was also found to correlate significantly with other academic performance-related factors such as academic self-efficacy and academic locus of control.

Clinical Training in Medical Schools

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There are various studies showing that learning styles, not just exam results, correlate to knowledge gained.

McManus, Richards, Winder, and Sproston (1998) found, in a large-scale prospective study of two cohorts of medical students at a London medical school, that the students’ learning styles, but not their final examination results, were related to the amount of knowledge gained from clinical experience. Using an abbreviated 18-item version of the Study Process Questionnaire (Biggs, 1987a), they reported positive correlations between strategic and deep learning styles and amount of knowledge gained from clinical experience.

Career Development

In reviewing weaknesses in current practices within industry toward the retention and development of individuals labeled as “high flyers,” Bates (1994) lists learning style as one key factor. Bates cites Honey and Mumford’s (1986) model of learning style as an appropriate model for individual learning and one capable of encompassing a framework for high flyer development. In the move to cultivate the top managers of the future, Bates calls for individual learning styles to be taken into account through the provision of a variety of learning situations, which should create the opportunity for the development of a full range of styles.

Police Training

In a review of existing methods of police training in the United States, Birzer (2003) criticizes traditional behavioral approaches in favor of instructional methods that recognize and call for a more student-centered approach to training in the future. [. . .]

Source: Cassidy, S. (2004). Learning styles: An overview of theories, models, and measures. Educational Psychology, 24(4), 419–444. Taylor & Francis, Ltd. Copyright © 2004 Routledge.

Learning styles, as a variable of successful learning and effective information processing, have been a topic of discussion since the mid-20th century. An increased awareness about one’s own learning preferences encourages us to promote individualized learning strategies to support effective knowledge acquisition and performance.

However, it is important to note that not all learning theorists (or research findings) support the use of learning styles to increase the effectiveness of learning (Pashler, McDaniel, Rohrer, & Bjork, 2008). Evidence has been presented that suggests that there is little to no improvement in learning when specific strategies are used to support learner styles (Cook, Thompson, Thomas, & Thomas, 2009; Massa & Mayer, 2006; Pashler et al., 2008). Cook and colleagues (2009) conducted research comparing instruction-specific strategies with no aligned strategies for learners who possessed a sensing learning style. The study results indicated that the specifically aligned instruction did not affect learning effectiveness. As an additional example, a study performed by Massa and Mayer (2006) found instruction that applied learning styles might not generate more effective learning. The study assessed technology strategies supporting verbal learners (with supplementary printed text) versus strategies supporting visual learners (with diagrams and illustrations). The findings indicated that corresponding instructional deliveries did not support more effective learning (Massa & Mayer, 2006). In other words, even though the support was specifically designed to meet learner preferences (visual or verbal), neither learner type showed increased learning success.

These types of study results indicate that one should be cautious about applying strategies that are developed for preferential learning styles. Some learning theorists do not agree that one’s learning style, rather than self-discipline or IQ, should be an indicator of more successful learning. However, learning styles have become a prevalent tool within educational contexts because learning styles can provide additional strategies to consider in discussions about how to increase learner success, with proven results (Graf, Kinshuk, & Liu, 2009).

The next section in this text will discuss technology and its effects on meaningful learning. As a tool, technology can offer supportive strategies in different learning contexts. Technological advancements continue to evolve at rapid speeds (Huston, 2013), so it is a good idea to consider the role technology can play in how we learn.

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7.5 Learning and Technology

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7.5 Learning and Technology

Although technology is a tool, not a framework or model, a discussion about its role in learning will support our understanding of how we learn because technology is a variable that affects meaningful learning.

As technology continues to change, many optional learning experiences have become available, including audio and visual aids such as videos, podcasts, interactive maps, and 3-D printing. These new technologies can be used to support different learning styles or preferences. For example, a student who prefers to use audio and visual options when learning about history might have an opportunity to supplement his or her textbook readings with additional video documentaries or interactive maps. However, technology and open access information can also have negative effects on learners (e.g., the information could be inaccurate or misunderstood, or learners’ schema development could be inefficient). With the increased use of technology, it is not uncommon for learners to passively consider computers (and other technologies) to be “answer finders.” Rather than becoming dependent on technology’s automated functions, learners should be encouraged to see technological devices as tools for supporting more effective learning and to be conscious users. Thus, an understanding of how we can use technology to foster learning is an essential component of understanding how we learn.

The series of excerpts in this section is from Jonassen, Howland, Marra, and Crismond (2008). Jonassen and colleagues give a thorough explanation of how technology has changed, the struggle to use it effectively for improved learning, and how it can be used to encourage more effective, meaningful, and authentic learning.

Excerpts from Meaningful Learning With Technology

By D. Jonassen, J. Howland, R. M. Marra, and D. Crismond

[. . .] Some of the first educational technologies, to support learning, were illustrations in 17th-century books and slate chalkboards in 18th-century classrooms. Educational technologies in the 20th century include lantern-slide and opaque projectors, later radio, and then motion pictures. During the 1950s, programmed instruction emerged as the first true educational technology, that is, the first technology developed specifically to meet educational needs. With every other technology, including computers, educators recognized its importance and debated how to apply each emerging commercial technology for educational purposes. Unfortunately, educators have almost always tried to use technologies to teach students in the same ways that teachers had always taught. So information was recorded in the technology (e.g., the content presented by films and television programs), and the technology presented that information to the students. The students’ role was to learn the information presented by the technology, just as they learned information presented by the teacher. The role of the technology was to deliver lessons to students, just as trucks deliver groceries to supermarkets (Clark, 1983). If you deliver groceries, people will eat. If you deliver instruction, students will learn. Not necessarily! We will tell you why later.

Technologies as Partners in Learning

The introduction of modern computer technologies in classrooms has followed the same pattern of use. Before the advent of microcomputers in the 1980s, mainframe computers were used to deliver drill and practice and simple tutorials for teaching students lessons. When microcomputers began populating classrooms, the natural inclination was to use them in the same way. A 1983 national survey of computer uses showed that drill and practice was the most common use of microcomputers (Becker, 1985).

Later in the 1980s, educators began to perceive the importance of computers as productivity tools. The growing popularity of word processing, databases, spreadsheets, graphics programs, and desktop publishing was enabling businesses to become more productive. So students in classrooms began word processing and using graphics packages and desktop publishing programs to write with. This tool conception pervaded computer use according to a 1993 study by Hadley and Sheingold that showed that well-informed teachers were extensively using text processing tools (word processors), analytic and information tools (especially databases and some spreadsheet use), and graphics tools (paint programs and desktop publishing) along with instructional software (including problem-solving programs along with drill and practice and tutorials).

The development of inexpensive multimedia computers and the eruption of the Internet in the mid-1990s quickly changed the nature of educational computing. Communications tools (e.g., email and computer conferences) and multimedia, little used according to Hadley and Sheingold, have dominated the role of technologies in the classroom ever since. But what are the students producing? Too often, they are using the technology to reproduce what the teacher or textbook told them or what they copy from the Internet.

Our conception of educational computing and technology use, described next, does not conceive of technologies as teachers or repositories of information. Rather, we believe that, in order to learn, students should teach the computer or use the technology to represent what they know rather than memorizing what teachers and textbooks tell them. Technologies provide rich and flexible media for representing what students know and what they are learning. A great deal of research on computers and other technologies has shown that they are no more effective at teaching students than teachers, but if we begin to think about technologies as learning tools that students learn with, not from, then the nature of student learning will change. [. . .]

To foster meaningful learning, one must consider changing from the idea of technology-as-teacher to technology-as-partner in the learning process. It is suggested that students do not learn from technology but that technologies can support productive thinking and meaning making by students. That will happen when students learn with the technology. But how do students learn with technologies? How can technologies become intellectual partners with students? We assume the following:

· Technology is more than hardware. Technology consists also of the designs and the environments that engage learners. Technology can also consist of any reliable technique or method for engaging learning, such as cognitive learning strategies and critical thinking skills. Learning technologies can be any environment or definable set of activities that engage learners in active, constructive, intentional, authentic, and cooperative learning.

· Technologies are not conveyors or communicators of meaning. Nor should they prescribe and control all of the learner interactions.

· Technologies support meaningful learning when they fulfill a learning need—when interactions with technologies are learner initiated and learner controlled and when interactions with the technologies are conceptually and intellectually engaging.

· Technologies should function as intellectual tool kits that enable learners to build more meaningful personal interpretations and representations of the world. These tool kits must support the intellectual functions that are required by a course of study. Learners and technologies should be intellectual partners, where the cognitive responsibility for performance is distributed by the part of the partnership that performs it better.

How Technologies Foster Learning

If technologies are used to foster meaningful learning, then they will not be used as delivery vehicles. Rather, technologies should be used as engagers and facilitators of thinking. Based on our conception of meaningful learning, we suggest the following roles for technologies in supporting meaningful learning:

· Technology as tools to support knowledge construction:

· for representing learners’ ideas, understandings, and beliefs

· for producing organized, multimedia knowledge bases by learners

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Technology plays a role in how modern humans learn. It can be a crutch for those who use computers or the Internet for finding answers rather than thinking critically or coming to their own conclusions. However, technology provides beneficial multimedia, communication tools, and more to facilitate learning.

· Technology as information vehicle for exploring knowledge to support learning by constructing:

· for accessing needed information

· for comparing perspectives, beliefs, and worldviews

· Technology as authentic context to support learning by doing:

· for representing and simulating meaningful real-world problems, situations, and contexts

· for representing beliefs, perspectives, arguments, and stories of others

· for defining a safe, controllable problem space for student thinking

· Technology as social medium to support learning by conversing:

· for collaborating with others

· for discussing, arguing, and building consensus among members of a community

· for supporting discourse among knowledge-building communities

· Technology as intellectual partner (Jonassen, 2000) to support learning by reflecting:

· for helping learners to articulate and represent what they know

· for reflecting on what they have learned and how they came to know it

· for supporting learners’ internal negotiations and meaning making

· for constructing personal representations of meaning

· for supporting mindful thinking

[. . .] Why do these uses of technology foster meaningful learning? It is because they require that students think and reason. Students do not learn from teachers or from technologies. Rather, students learn from thinking—thinking about what they are doing or what they did, thinking about what they believe, thinking about what others have done and believe, thinking about the thinking processes they use—just thinking and reasoning. Thinking mediates learning. Learning results from thinking. [. . .]

Using technologies to express and convey learner knowledge all entail different kinds of problem solving. Learning with technologies requires that students make myriad decisions while constructing their representations. Deciding what information to include and exclude, how to structure the information, and what form it should take are all complex decision-making processes. Students also engage in a lot of design problem solving while constructing their interpretations. They also must solve rule-using problems in how to use software. When learners are solving problems, they are thinking deeply and are engaged in meaningful learning. What they learn while doing so will be so much better understood and remembered than continuously preparing to answer multiple-choice test questions. [. . .]

Source: Jonassen, David H.; Howland, Jane L.; Marra, Rose M.; Crismond, David P., Meaningful Learning With Technology, 3rd Ed., © 2008. Reprinted by permission of Pearson Education, Inc., New York, New York.

Technology (and its rapid advancement) prompts theorists to consider its effects on how we learn. As discussed, technology can be a useful learning tool, but it can also negatively affect schema development, critical thinking, and reflection. A better understanding of how best to integrate technology into one’s learning experiences will continue to be assessed by researchers and is a valid concept, for anyone interested in increased learning performance, to consider when evaluating how we can each learn more effectively.

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Summary & Resources

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Summary & Resources

Chapter Summary

To better understand how we learn, it is important to also understand why ideas, suggestions, and research areas evolve. Intelligence (IQ, EI, and MI), learning styles, and applications of technology are all controversial areas of psychology, but they offer plausible ideas that can be applied in numerous ways to learning theories and sub-theories.

Scholars pose a diverse range of ideas about intelligence. The controversy in this area of study exists mostly because there is not a clear consensus about how to define or measure intelligence. Consensus about intelligence broadens as theories, models, and technology advance and evolve. The advancement in ideas, models, and theoretical frameworks associated with intelligence offer a useful perspective that we can apply to our knowledge acquisition about learning.

Through the lens of IQ, intelligence is a physiological attribute with clear definitions of below-average, average, and above average. In contrast, the suggestions that intelligence should incorporate other factors such as environmental, cultural, and personality characteristics also have important implications for human learning. These less stringent ideas about intelligence are quite different from previous definitions and thus stimulate further debates about potential learning styles and the effects of using technology to advance learning effectiveness.

It is not unusual to read studies whose findings clearly question the work and findings of other scholars. However, each has its usefulness in the overarching quest to understand how we learn and how we learn most effectively. Perhaps when each model is looked at in a way that complements rather than counters one another, then researchers and scholars can debate about how to effectively and more holistically advance the human ability to learn.

Learning styles offer another intriguing area of study. If we know that our minds process information in a specific way, how is it possible that preferred ways of learning affect knowledge acquisition? Or do they? Can we use this knowledge in combination with other theories to enhance the learning experience?

In earlier discussions about cognitivism, we learned that the information that we attend to more easily is more effectively moved from short-term to long-term memory. If a learner is more successful when he or she learns via a preferred method, couldn’t the learner use this to help decrease his or her cognitive overload (CLT), which is suggested to affect efficient learning (see Chapter 3)? From the constructivist lens, if the learner’s reality is that he or she learns more effectively one way than another, it could be inferred that this preference supports the assimilation of information into his or her personally perceived realities more effectively. In other words, is it a self-fulfilling prophecy? If the learner believes that he or she learns one way better than another, then could it be plausible that this belief might influence reality?

Though not a style or an intelligence, technology also offers additional opportunities for meaningful learning. Some research has suggested that technology can affect learning, both positively and negatively. But technology should be intentionally used to engage and foster thinking, rather than for shortcuts, providing supplements that support learners’ cognitive and motivational needs.

Key Ideas

· The Binet-Simon intelligence scale (an IQ assessment) was originally developed to identify children with learning disabilities.

· Defining what constitutes human intelligence has been a controversial process among academics.

· A key variable in defining intelligence is one’s ability to effectively adapt new knowledge to existing knowledge.

· A person’s IQ correlates with SAT and ACT scores, as well as career choices.

· There is intense debate about whether or not the environment affects one’s IQ.

· There is one identified biological variable that affects IQ: breastfeeding.

· Although unproven, stress has been suggested to have potential effects on brain development, and thus IQ.

· IQ tends to be higher in high SES persons versus lower SES persons.

· Fluid intelligence (g[F]) evaluates spontaneous learning, reasoning, and problem solving.

· Crystallized intelligence (g[C]) assesses the present knowledge one has developed.

· The mean score on the IQ assessment is always set at 100.

· The multiple intelligences (MI) model suggests that humans possess different abilities and identifies these diverse ability areas as “intelligences.”

· There are nine identified multiple intelligences, and these areas are more holistic in nature.

· In defining intelligence, the multiple intelligences model includes qualities such as optimism, self-control, and moral character. This contrasts with IQ, which postulates these variables as personality characteristics.

· Emotional intelligence (EI) identifies four key variables:self-awareness, self-management, social awareness, and relationship management.

· EI measures self-competencies as well as one’s social competencies.

· MI and EI reflect a detachment from traditional theories of intelligence, suggesting increased subjectivity is necessary when defining intelligence.

· Learning styles (LS) are the ways in which individuals characteristically approach different learning tasks (e.g., intuitively, reflectively, strategically, or apathetically).

· Cognitive styles (CS) are the ways in which a person thinks, reasons, or problem solves.

· To learn effectively using technology, humans must see technology as a tool, not as an informational expert.

· Any environment or activity designed to intentionally engage learners in more meaningful learning is considered a learning technology.

· Technology affects effective knowledge acquisition both positively and negatively.

Additional Resources

Understanding the different ways that the scholarly community defines and applies intelligence is vital in developing a clear understanding of how learning styles and preferences for specific learning strategies can effectively support our own knowledge acquisition, as well as that of others. The following resources can be used to further your understanding of the topics that were introduced in this chapter. Some resources may also be accessible via your university library.

Intelligence

· Fraser, S. (1995). The bell curve wars: Race, intelligence, and the future of America. New York, NY: Basic Books.

· Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York, NY: Free Press.

· Mensh, E., & Mensh, H. (1991). The IQ mythology: Class, race, gender, and inequality. Carbondale, IL: Southern Illinois University Press.

· Seligman, D. (1992). A question of intelligence: The IQ debate in America. New York, NY: Birch Lane Press.

· Binet, A. (1905). New methods of diagnosis of the intellectual level of subnormals. First published in L’Année Psychologique, 12, 191–244. Retrieved from http://psych classics.yorku.ca/Binet/binet1.htm

· Sternberg, R. J. (2015). Human intelligence. In Encyclopedia Britannica online. Retrieved from https://www.britannica.com/topic/human-intelligence-psychology

Multiple Intelligences

· Cherry, K. (2016, October 3). Gardner’s theory of multiple intelligences. Very Well. Retrieved from http://psychology.about.com/od/educationalpsychology/ss /multiple-intell_2.htm

· Cherry, K. (2016, May 9). Howard Gardner biography. Very Well. Retrieved from http://psychology.about.com/od/profilesal/p/howard-gardner.htm

· Gardner, H. (n.d.). Frequently asked questions. Retrieved from http://howard gardner.com/faq/

· Gardner, H. (1993). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books.

· Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York, NY: Basic Books.

· Gardner, H. (2006). Multiple intelligences: New horizons. New York, NY: Basic Books.

· Hoerr, T. R. (2000). The theory of multiple intelligences. In Becoming a multiple intelligences school. Alexandria, VA: Association for Supervision and Curriculum Development (ASCD). Retrieved from http://www.ascd.org/publications/books/100006 /chapters/The-Theory-of-Multiple-Intelligences.aspx

· McKnight, H. (2011). Multiple intelligences [Video file]. Retrieved from http://youtu.be/cf6lqfNTmaM

· Strauss, V. (2013, October 16). Howard Gardner: ‘Multiple intelligences’ are not ‘learning styles.’ The Washington Post. Retrieved from http://www.washingtonpost .com/blogs/answer-sheet/wp/2013/10/16/howard-gardner

Emotional Intelligence

· Brackett, M., Rivers, S. E., & Salovey, P. (2011). Emotional intelligence: Implications for personal, social, academic, and workplace success. Social and Personality Psychology Compass, 5(1), 88–103. Retrieved from http://ei.yale.edu/wp-content/uploads /2013/09/pub184_Brackett_Rivers_Salovey_2011_Compass-1.pdf

· Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. New York, NY: Bantam Publishing Group.

· Gerber, W. (1969). Human intelligence. In Editorial research reports 1969 (Vol. II). Washington, D.C.: CQ Press. Retrieved from CQ Press Electronic Library, CQ Researcher Online, http://library.cqpress.com/cqresearcher/cqresrre1969082000

· Mayer, J. D., Caruso, D., & Salovey, P. (1999). Emotional intelligence meets traditional standards for an intelligence. Intelligence, 27, 267–298.

· Mayer, J. D., Salovey, P., & Caruso, D. R. (2000). Models of emotional intelligence. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 396–420). Cambridge, UK:Cambridge University Press.

· Thorndike, E. (1927). The measurement of intelligence. New York, NY: Teachers College Press.

Learning Styles

· Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school (expanded edition). Washington, D.C.: National Academy Press.

· Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245–281.

· Chick, N. (2011). Understanding learning styles: A conversation with Dr. Bill Cerbin. Interview with Nancy Chick [Audio podcast]. University of Wisconsin Colleges Virtual Teaching and Learning Center. Retrieved from https://sites.google.com/a /gapps.uwc.edu/vtlc/home/programs/podcasts/learningstylescerbin

· Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London, UK: Learning and Skills Research Centre.

· Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 103–119. Retrieved from http://www.psychologicalscience.org/journals/pspi/PSPI_9_3.pdf

Key Terms

Binet-Simon intelligence scale

cognitive styles

crystallized intelligence ( g[C])

emotional intelligence (EI)

fluid intelligence ( g[F])

Flynn effect

general factor of intelligence ( g)

intelligence

intelligence quotient (IQ)

learning strategies

learning styles (LS)

learning technologies

multiple intelligences (MI)

practical intelligence

triarchic intelligence theory

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