Learning Styles and Cognitive Styles PDF

5 LEARNING STYLES AND COGNITIVE STYLES This chapter offers a real contrast to what we observed in the previous chapter on motivation: As we saw there, over the past 10 years the field of L2 motivation theory and research has been transformed by a surge of activity; as we will see in the current cha...

5 LEARNING STYLES AND COGNITIVE STYLES This chapter offers a real contrast to what we observed in the previous chapter on motivation: As we saw there, over the past 10 years the field of L2 motivation theory and research has been transformed by a surge of activity; as we will see in the current chapter, with regard to the concepts of learning and cognitive styles time seems to have frozen and the topic has hardly attracted any serious scholarly attention over the same period. Theoretically, we are very much ‘as you were’ when looking back to the 2005 account, and in terms of research output, styles have slipped off the radar of the current agenda. Nevertheless, despite the limited interest or new thinking in the area, we do feel there is an important lesson bur- ied beneath the headline; the story of how and why styles have failed to excite interest is one that may help us understand how our field is developing and the directions we may be going in the future. The concept of learning styles attempts to explain how people learn in differ- ent ways and how we all have our own preferred, thus more effective, ways of learning. Over the years, the concept of learning styles steadily gained influence and acceptance, not only among educators but also among the general public. Unsurprisingly, this interest extended to the field of SLA, where the concept has been treated with respect, as an important, although somewhat under-researched topic. In this chapter we look at main conceptual issues related to learning styles, including some of the controversies surrounding the concept, and as will become clear, these controversies are not confined to L2 studies but reflect a similar picture in the field of educational psychology. Despite the broad mainstream acceptance of the importance of learning styles, the academic consensus has been much less favorable, with even one of the main authorities in cognitive styles research conceding that “the area of style research generally has a poor reputa- tion” (Riding, 2000a, p. 316). As Riding explains, this is because this research  Learning Styles and Cognitive Styles 107 area has suffered from a number of serious problems, particularly with respect to four key aspects: Workers in this area have been remiss in that they have: generated a large and bewildering array of labels purporting to being different styles, used ineffective and questionable assessment methods, not made a clear distinc- tion between style and other constructs such as intelligence and personal- ity, and have been slow to demonstrate the practical utility of style. (Riding, 2000b, p. 368) Coffield (2005) is even more disparaging of the concept: “The field of learning styles suffers from almost fatal flaws of theoretical incoherence and conceptual confusion” (p. 21), which raises the question: Why discuss learning styles at all? The simple answer is that there is something genuinely appealing about the notion and, what is more, this intuitive appeal tends to resonate strongly with the class- room experience of educational practitioners. The powerful attraction of styles as a concept is summed up by Griffiths (2012), who contends that the concept has the potential to greatly enhance learning and to make learning more enjoyable and successful. It is a concept that acknowledges individual dif- ferences, rather than seeing all learners as similar. For teachers, it presents an opportunity to offer students methodologies and materials appropri- ate to their own learning style preferences. For learners, it allows them the freedom to learn in ways which are enjoyable and can help them to become the best that they are capable of. (p. 151) Who could fail to be enticed by such promise? The hope underpinning much research into styles is that the current state of confusion is merely due to our insufficient knowledge rather than the scientific inadequacy of the concept, and that further inquiry will reveal a more robust concept enabling both educators and learners to realize its potential. The 2005 view of styles research and theory was that it was a field based more on hope than substance, and this evaluation still stands, although in 2015 we would suggest that the hope of building up sufficient knowledge to eliminate the theoretical confusion has begun to fade. A further illuminating issue that emerges from a consideration of learning styles is the tension the topic reveals between academic theory and pedagogic practice. Seasoned classroom practitioners are likely to argue that styles are indeed very real and are a key aspect of successful learning, whereas a rigorous theorist is more likely to take the view that the concept of styles falls apart under any form of scientific scrutiny. In this sense cognitive/learning styles are not dissimilar to ID factors in general in terms of the uncertainty of their exact definition as well as the ‘I can live neither with you, nor without you’ attitude that many experts share toward them. 108 Learning Styles and Cognitive Styles What Are Learning Styles? As is the case with many ID variables, learning styles, though initially seem- ingly straightforward and intuitively convincing, turn out to be problematic under close scrutiny. According to the standard definition, they refer to “an individual’s natural, habitual, and preferred way(s) of absorbing, processing, and retaining new information and skills” (Reid, 1995, p. viii); thus, they are “broad preferences for going about the business of learning” (Ehrman, 1996, p. 49). As such, the concept represents a profile of the individual’s approach to learning, a blueprint of the habitual or preferred way the individual perceives, interacts with, and responds to the learning environment. These definitions make intui- tive sense: Few would question that different learners can approach the same learning task in quite different ways and it is also a logical assumption that this variation in approach is not infinite but is characterized by systematic patterns. These patterns, then, can be rightfully called ‘learning styles.’ Thus, at this intuitive level, the concept of styles is relatively uncontroversial. It is only when we attempt to analyze its theoretical underpinnings that the concept becomes problematic, since “learning style is often used as a metaphor for considering the range of individual differences in learning” (Price, 2004, p. 681). There is a confusing plethora of labels and style dimensions (e.g., Peter- son, DeCato, & Kolb, 2014, suggest a figure of around 100 established styles frameworks and assessments); there is a shortage of valid and reliable measure- ment instruments; there is confusion in the underlying theory; and the practical implications put forward in the literature are scarce and rather mixed, and quite frankly rarely helpful. In a particularly thorough (and rather critical) review of the literature, Coffield, Moseley, Hall, and Ecclestone (2004) found a total of 71 different learning style models, which they subdivided into 13 major models and 58 minor, and then further categorized these into five principal families, which we summarize in Table 5.1. It is not our intention to discuss all of these models of learning styles, but we need to give some indication of the diversity of perspectives encountered in the styles literature, for this has been such a persistent criticism of the field. TABLE 5.1 Major families of learning styles and the main scholars associated with them (adapted from Coffield et al., 2004) Physiologically based learning styles (including the four modalities: visual, auditory, kinesthetic, tactile): Dunn, Dunn, and Price (1975), Gregorc (1979) Learning styles based on cognitive structure: Riding (2000a) Personality-based learning styles: Apter (1976), Myers and Briggs (1976) Flexibly stable learning preferences: Allinson and Hayes (1988), Herrmann (1989), Honey and Mumford (1992), Kolb (1984) Learning styles as approaches, strategies, or orientations: Entwistle (1990), Sternberg (1999), Vermunt (1998)  Learning Styles and Cognitive Styles 109 For example, Ivie (2009, p. 178) amusingly refers to a “Humpty Dumpty model of education” and as the author goes on to argue, the term styles has become so vague and imprecise that advocates of learning styles are reminiscent of the char- acter from Alice Through the Looking Glass who declared, “When I use a word, it means just what I choose it to mean.” Nevertheless, despite the bewilder- ing range of models and conceptualizations, learning styles remain an appealing concept for educationalists because—unlike abilities and aptitudes—they do not reflect an innate endowment that automatically leads to success. That is, styles are not yet another metaphor for distinguishing the gifted from the untalented, but rather they refer to personal preferences. These preferences are typically bipolar, representing a continuum from one extreme to another (e.g., being more global vs. being more particular), and no value judgment is made about where a learner falls on the continuum: One can be successful in every style position—only in a different way. In Chapter 3, we observed how the concept of aptitude had fallen out of favor with the advent of more communicative approaches to language education; in contrast, the concept of learning styles offers a more democratic stance that is in tune with the spirit of the times, a “value-neutral approach for understanding individual differences among linguistically and culturally diverse students” (Kinsella, 1995, p. 171). The continuing appeal and popularity of the concept of learning styles tells us a lot—not all of it good—about the nature of education in the 21st century. In a scholarly review of the popular appeal of learning styles, Pashler, McDan- iel, Rohrer, and Bjork (2009) identify several key factors behind its rise. One of these factors is that the concept of styles is essentially a ‘type theory’ and there seems to be some enduring popular appeal in finding out ‘what is my type’—people find such theories very difficult to resist. Additionally, people in consumer-oriented societies can be attracted to the idea that they, and their children, are unique, and that learning should be tailored to their own indi- vidual requirements. Related to this is the fact that styles provide a ready-made excuse for any failure to learn: “Rather than attribute one’s lack of success to any lack of ability or effort on one’s part, it may be more appealing to think that the fault lies with instruction being inadequately tailored to one’s learn- ing style” (p. 108). Furthermore, we cannot ignore the commercial realities of education and recognize that promoting learning styles in various forms (e.g., in-service courses, publications, inventories) has become a thriving commercial area, with powerful entrepreneurial forces endorsing the concept. With these sobering thoughts in mind, let us consider some fundamental conceptual issues relating to learning styles. Basic Conceptual Issues It is useful to start the discussion by addressing what the relationship is between learning styles and learning strategies. In the SLA literature, there has been a 110 Learning Styles and Cognitive Styles considerable amount of overlap ever since Stern (1975) positioned a ‘personal learning style’ at the top of his list of strategies employed by good language learn- ers. The two concepts are thematically related since they both denote specific ways learners go about carrying out learning tasks. This has been well reflected by a recent attempt to establish consensus on the definitions of cognitive style and learning style within the international styles research community: After a four-phase process of iterative fine-tuning of views obtained from a substantial group of scholars (N = 65), Armstrong, Peterson, and Rayner (2012) produced the following ultimate definition of learning style: Learning styles are individuals’ preferred ways of responding (cognitively and behaviorally) to learning tasks which change depending on the environment or context. They can affect a person’s motivation and attitude to learning, and shape their performance. (p. 451; emphasis added) According to Snow et al. (1996), the main difference between the two concepts—learning styles and strategies—lies in their breadth and stability, with a style being a “strategy used consistently across a class of tasks” (p. 281). Refer- ring back to Table 5.1, some of the leading models envisage a physiological basis (Dunn et al., 1975; Gregorc, 1979; Riding, 2000a) and regard styles as being fixed within the individual—“We can no sooner change our styles than permanently change the color of our eyes, hair, or skin” (DeBello, 1990, p. 218)—whereas strategies may be learned and developed in order to cope with situations and tasks. Sternberg and Grigorenko (2001) further highlight the difference between the degree of consciousness involved in applying styles and strategies: Styles operate without individual awareness, whereas strategies involve a conscious choice of alternatives. As the authors conclude, although the two terms are often mixed up, “strategy is used for task- or context-dependent situations, whereas style implies a higher degree of stability falling midway between ability and strategy” (p. 3). In specific reference to the field of language learning, Bailey, Onwuegbuzie, and Daley (2000, p. 118) concur: “Learning styles are not the same as learning strategies.... Whereas learning styles represent unintentional, or automatic individual characteristics, learning strategies are actions chosen by students that are intended to facilitate learning.” On the whole, the argument that styles are stable and have a cross-situational impact sounds convincing, but if we take a closer look we find that there is a definite interaction between styles and situations; as Ehrman (1996, p. 53) has put it succinctly, “Just as situations determine which hand to use (write with one hand, grip jars to open with the other), so they also have considerable influence on choice of learning strategies associated with one learning style or another.” This observation has also been borne out by research, and in a review of the rel- evant literature, Kozhevnikov (2007, p. 477) concludes that “cognitive styles are  Learning Styles and Cognitive Styles 111 not simply inborn structures, dependent only on an individual’s internal charac- teristics, but, rather, are interactive constructs that develop in response to social, educational, professional, and other environmental requirements.” Furthermore, the stability aspect of styles has also been questioned when researchers found that early educational experiences shape one’s individual learning styles by instilling positive attitudes toward certain sets of learning skills and, more generally, by teaching students how to learn (Kolb, Boyatzis, & Mainemelis, 2001). Indeed, Mandelman and Grigorenko (2012) conclude that heritability estimates for styles tend to be lower than those for either intelligence or personality (from 0% to ~ 30%), indicating the larger influence of nongenetic, situational factors. We also hit shaky ground when we try to analyze what exactly the term ‘preference’ means when we talk about styles being ‘broad learning preferences.’ How much do these ‘preferences’ determine our functioning? Ehrman (1996) suggested a relatively soft interpretation of ‘preference’ by equating it with ‘com- fort zones’: “For most of us, a preference is just that—something we find more comfortable but can do another way if circumstances require it” (p. 54). As she explained, however, for a minority, learning styles are more firmly set and are therefore more than mere preferences. These individuals do not have the flex- ibility to change or shift their employed style according to the demands of the situation, and this may land them in trouble. According to Ehrman, a learning style, then, can range from a mild preference to a strong need. The stable-yet- flexible quality of learning styles has been further emphasized by Oxford (2011, p. 40), who argues that “although the learner may have some strong style tenden- cies, they are not set in stone.” How do learning styles relate to other core individual differences such as person- ality and cognitive abilities? This, again, is a source of considerable controversy— usually referred to as the ‘style overlap’ (Zhang, Steinberg, & Rayner, 2012)—because certain well-known psychological constructs are sometimes referred to as learn- ing styles and sometimes as personality dimensions. The extraversion–introversion dimension is a good example, as this popular dichotomy, first brought into wide use by Swiss psychologist Carl Jung, can be found in almost every personality and learning style taxonomy. Similarly, as we will see later in this chapter, there are conceptualizations of learning styles that appear to be very closely connected to cognitive abilities. In fact, in their discussion of styles, Sternberg, Grigorenko, and Zhang (2008) argue that there are two primary categories of learning styles: ‘personality-based learning styles,’ and ‘ability-based learning styles’—if this is the case, then is it really possible to consider styles as individual differences in their own right? In sum, the above outline of various style issues conveys well the general impression one gains when dealing with learning styles, namely that they are elusive, ‘halfway’ products: They refer to preferences, but these can be of vary- ing degree; they are related to learning strategies but are somewhat different from them as they fall midway between innate abilities and strategies; they 112 Learning Styles and Cognitive Styles appear to be situation-independent but they are not entirely free of situational influences; and some style dimensions are also listed as major components of personality. Indeed, learning styles appear to have very soft boundaries, making the category rather open-ended, regardless of which perspective we approach it from. The 2005 version of this section concluded by quoting Ehrman, Leaver, and Oxford, (2003) summary, and that summary is, regrettably, still valid today: “The literature on learning styles uses the terms learning style, cognitive style, personality type, sensory preference, modality, and others rather loosely and often interchangeably” (p. 314). Such a lack of uniformity inevitably raises doubts about the concept of learning styles: Is it really more than a convenient way of referring to certain patterns of information processing and learning behaviors whose antecedents lie in a wide range of diverse factors, such as varying degrees of acquired abilities and skills, idiosyncratic personality features, and different exposures to past learning experiences? In order to bring some clarity to the issue, let us start by making a distinction between learning styles and cognitive styles. Cognitive Styles As Rayner (2000) summarized, if learning style is represented as a profile of the individual’s approach to learning, this profile can be seen to comprise two fun- damental levels of functioning: The first is cognitive, referring to a stable and internalized dimension related to the way a person thinks or processes informa- tion; the second is the level of the learning activity, which is more external and embraces less stable functions that relate to the learner’s continuing adaptation to the environment. From this perspective, therefore, the core of a learning style is the ‘cognitive style,’ which can be seen as a partially biologically determined and pervasive way of responding to information and situations; and when such cognitive styles are specifically related to an educational context and are inter- mingled with a number of affective, physiological, and behavioral factors, they are usually more generally referred to as learning styles (Brown, 2000). In our quest to understand the nature of learning styles, therefore, we need to take a step back and start with the analysis of cognitive styles. Cognitive styles are usually defined as an individual’s preferred and habitual modes of perceiving, remembering, organizing, processing, and representing information. In their attempt to achieve a consensus in definition (mentioned above), Armstrong and his colleagues (2012) produced the following ultimate definition: Cognitive styles refer to individual differences in people’s preferred way of processing (perceiving, organizing and analyzing) information using cognitive brain-based mechanisms and structures. They are assumed to be relatively stable and possibly innate. Whilst cognitive styles can influence  Learning Styles and Cognitive Styles 113 a person’s behavior, other processing strategies may at times be employed depending on task demands—this is because they are only preferences. (p. 451) The advantage of focusing on cognitive styles prior to learning styles is that the former are devoid of any educational and situational/environmental interfer- ences, thereby allowing for a ‘purer’ definition. Yet, as we will see next, this is still only a partial solution to the style ambiguity because we find an unspeci- fied or ‘fluid’ relationship between cognitive styles and personality on the one hand, and between cognitive styles and cognitive abilities on the other. Thus, cognitive styles are typically characterized as being in a “conceptual gray area” (Hampson & Colman, 1994, p. x) between personality and intelligence, and are expected to explain variance beyond both of these variables. Research on cognitive styles goes back to the end of the 19th century when scholars noticed that some people had a predominantly verbal way of represent- ing information in thought, whereas others were more visual or imaginal (cf. Riding, 2000a; for a recent historical review, see Nielsen, 2012). There have been ongoing investigations on styles ever since, but styles research took off in the 1940s and 1950s, when Witkin and his colleagues initiated work on the study of field dependence–independence (see later in detail). During the subsequent decades, scholars identified an ever-increasing number of cognitive style dimensions, but the validity of such an extensive range of styles became the subject of a great deal of debate toward the end of the 20th century, with some scholars claiming that the different style labels did not reflect genuine differences and therefore most identified styles could be grouped into far fewer principal cognitive style dimen- sions (Riding, 2000a). Problems with the Notion of Cognitive Style The scope of the problem with cognitive styles becomes obvious when we con- sider the long list of cognitive style dichotomies in Table 5.2, identified by Cof- field et al.’s (2004) systematic survey. As these researchers concluded, The sheer number of dichotomies betokens a serious failure of accumulated theoretical coherence... there is some overlap among the concepts used, but no direct or easy comparability between approaches; there is no agreed ‘core’ technical vocabulary. The outcome—the constant generation of new approaches, each with its own language—is both bewildering and off-putting to practitioners and to other academics who do not specialize in this field. (p. 136) Although the theoretical basis of cognitive styles is more solid than that of learning styles, even cognitive styles have been subject to a lot of criticism, 114 Learning Styles and Cognitive Styles TABLE 5.2 Cognitive style dichotomies identified by Coffield et al.’s (2004, p. 136) systematic survey of learning styles convergers vs. divergers intuitionists vs. analysts verbalizers vs. imagers extroverts vs. introverts holists vs. serialists sensing vs. intuition deep vs. surface learning thinking vs. feeling activists vs. reflectors judging vs. perceiving pragmatists vs. theorists left brainers vs. right brainers adaptors vs. innovators meaning-directed vs. undirected assimilators vs. explorers theorists vs. humanitarians field dependent vs. field independent activists vs. theorists globalists vs. analysts pragmatists vs. reflectors assimilators vs. accommodators organizers vs. innovators imaginative vs. analytic learners lefts/analytics/inductives/ non-committers vs. plungers successive processors vs. rights/ common-sense vs. dynamic learners globals/deductives/ simultaneous concrete vs. abstract learners processors random vs. sequential learners executive, hierarchic, conservative initiators vs. reasoners vs. legislative, anarchic, liberal which never allowed for the concept to take a substantial place in mainstream cognitive psychology. The crux of the problem is that styles research in the past has not been able to demonstrate sufficiently that the notion of cogni- tive style is a theoretical construct in its own right, and thus the concept has become, in Sternberg and Grigorenko’s (2001) words, too “instrument- bound.” That is, a style was what a particular style questionnaire measured, which is a recurring issue in ID research, as we have found the same phenom- enon in the domain of language aptitude research. And since most researchers produced their own idiosyncratic instruments, resulting in their own idiosyn- cratic style conceptualizations, these overlapping concepts could not converge sufficiently, thereby creating a rather confused and confusing overall picture. This was coupled with the fact that many of the actually identified and mea- sured style dimensions were not sufficiently separate from certain ability and personality characteristics; for example, the MBTI personality types tend also to be listed as cognitive style dichotomies (as in Table 5.3), and the problem of overlap even led to the fall of the most famous cognitive style dimension, field dependence–independence, as it was found to correlate excessively with spatial intelligence. Leading Models of Styles and Their Assessment Having argued that the proliferation of conceptualizations of styles has been both confusing and unhelpful, we will refrain from presenting a comprehensive, thus confusing and unhelpful, account of these various theories. Instead, we will  Learning Styles and Cognitive Styles 115 concentrate on just two leading models—Riding’s and Kolb’s—with the aim of illustrating some of the key characteristics, both in terms of strengths and flaws, associated with styles theory and research. Riding’s System Richard Riding has been one of the main international proponents of cogni- tive styles research. Aware of the manifold problems that have undermined this research domain, he proposed a powerful and parsimonious system of cogni- tive styles that, in his and his followers’ view, remedied the shortcomings of past styles research while maintaining the attractive features of the concept. The proposed taxonomy postulates only two superordinate style dimensions that subsume most of the previously proposed constructs (for a summary, see Table 5.3): Wholist–Analytic Style dimension, determining whether individuals tend to organize information as an integrated whole or in discrete parts of that whole (i.e., take a whole view or see things in parts). Verbal–Imagery Style dimension, determining whether individuals are out- going and inclined to represent information during thinking verbally or whether they are more inward and tend to think in mental pictures or images; in other words, verbalizers are superior at working with verbal information, whereas imagers are better at working with visual or spatial information. According to Riding (2002), wholists tend to see a situation as a whole (hence the label), have an overall perspective, and appreciate the total context. Wholists therefore are ‘big picture people’ and therefore they can also easily lose sight of the details. When presented with a prose passage for recall, for example, wholists will do best when the title of the passage is given before rather than after the passage is presented because this title will provide them with an overall thematic orientation. Analytics, on the other hand, see a situation as a collection of parts, often focusing on one or two aspects only, and therefore providing the title of the reading passage will not enhance their performance substantially. Their strength is that they can separate out a situation into its parts, which allows them to come quickly to the heart of any problem. They are also good at seeing similarities and detecting differences. The danger for analytics, on the other hand, is that they may get the particular aspects that they focus on out of proportion, and thus may not get a balanced view. The verbal–imagery style dimension concerns the way information is represented as well as the external and internal focus of attention. The former aspect refers to the extent to which one constructs mental pictures when reading or thinking, rather than thinking in words. The latter aspect has implications for social rela- tionships: Verbalizers tend to focus outward and prefer a stimulating environment, 116 Learning Styles and Cognitive Styles TABLE 5.3 List of the major cognitive style constructs that Riding’s two fundamental style dimensions subsume (adapted from Riding & Rayner, 1998) The wholist–analytic dimension Field dependence– Individual dependence on a perceptual field when Independence analyzing a structure or form that is part of the field. Leveling–Sharpening A tendency to assimilate detail rapidly and lose detail or emphasize detail and changes in new information. Impulsivity–Reflectiveness Tendency for a quick vs. deliberate response. Converging–Diverging Narrow, focused, logical, deductive thinking rather than Thinking broad, open-ended, associational thinking to solve problems. Holist–Serialist Thinking The tendency to work through learning tasks or problem- solving incrementally or globally and assimilate detail. Concrete Sequential/ The tendency to learn through concrete experience and Concrete Random/ abstraction either randomly or sequentially. Abstract Sequential/ Abstract Random Assimilator–Explorer Individual preferences for seeking familiarity or novelty in the processes of problem-solving and creativity. Adaptors–Innovators Adaptors prefer conventional, established procedures, whereas innovators favor restructuring or new perspectives in problem-solving. Reasoning–Intuitive/ Preference for developing understanding through reasoning Active–Contemplative or by spontaneity/insight and learning activities that allow active participation or passive reflection. The verbal–imagery dimension Abstract vs. Concrete Preferred level and capacity of abstraction. Thinker Verbalizer–Visualizer The extent to which verbal or visual strategies are used in thinking and to represent knowledge. whereas imagers tend to be more passive with an inward focus, content with a static environment. Of course, most people are somewhere in between the two extremes with regard to the two style dimensions, often being able to benefit from the advantages of both. And, to complicate things further, the two style dimensions interact with each other, resulting in various combination patterns. In spite of these reservations, Riding’s approach of creating a hierarchy of multiple levels of styles represents one of the most promising directions out of the theoretical style-maze. Kozhevnikov (2007, p. 477) has referred to such higher- order styles as “metastyles” and describes them as “superordinate styles govern- ing an individual’s flexibility in the use of subordinate styles, depending on the requirements of a task.” As she continues, research has “empirically confirmed that cognitive styles are based on neither a single underlying dimension nor operation in isolation but rather that there is a structural relation among them.”  Learning Styles and Cognitive Styles 117 The two superordinate style dimensions in Riding’s theory are fully compatible with these research findings. Kolb’s Model of Learning Styles Having reviewed briefly a ‘pure’ cognitive style system, let us now return to the broader issue of learning styles. Although there are a number of competing models in the literature (see Table 5.1), we have chosen to focus on the theory proposed by Kolb (1984; Kolb, Boyatzis, & Mainemelis, 2001; Kolb & Kolb, 2005a) as part of his broader experiential learning theory because (a) it is a theory that has been widely endorsed by both researchers and practitioners, with Kolb and Kolb (1999) report- ing 1,004 separate studies based on his model; (b) it is a theory that highlights both the potentials and the limitations of learning styles; and (c) it is connected to an influential assessment instrument, which we examine later in the chapter. According to Kolb (1984), “Learning is the process whereby knowledge is created through the transformation of experience. Knowledge results from the combina- tion of grasping experience and transforming it” (p. 41), and Kolb’s classic learning style construct was based on the permutation of two main dimensions, concrete vs. abstract thinking and active vs. reflective information processing. An orientation toward concrete thinking focuses on being involved in experiences and dealing with immedi- ate human situations in a personal way, emphasizing feeling as opposed to thinking. An orientation toward abstract conceptualization focuses on using logic, ideas, and concepts, emphasizing thinking as opposed to feeling. An orientation toward active experimentation focuses on actively influencing people and changing situations; it emphasizes practical applications as opposed to reflective understanding. An ori- entation toward reflective observation focuses on understanding the meaning of ideas and situations by carefully observing and impartially describing them; it emphasizes understanding as opposed to practical application. Based on the combination of the two style continua, four basic learner types, or learning style patterns, emerge: Divergers (concrete & reflective) have received their label because they prefer concrete situations that call for the generation of ideas, such as a brainstorm- ing session. This does not mean they are abstract thinkers; just the opposite, they are down-to-earth people who learn best through concrete experience and like to look at concrete situations from many points of view in a reflec- tive manner. They are also interested in other people and are fairly emotional in their dealings with them. They have broad cultural interests and often specialize in the arts. In classroom situations they prefer to work in groups. Convergers (abstract & active) are abstract thinkers who generate ideas and theories. They are, however, not detached from reality, as they are interested in active experimentation to find practical uses for their schemes. They are good at solving specific problems, especially if the tasks are technical rather than interpersonal or social in nature. In formal learning situations, people 118 Learning Styles and Cognitive Styles with this style prefer experiments and simulations, laboratory assignments, and practical applications. Assimilators (abstract & reflective) are also abstract thinkers but their strength is not in dreaming up ideas and then actively trying to put them to test, like that of convergers, but rather, as the name suggests, assimilating disparate observations in a reflective manner, that is, understanding a wide range of information and putting it into a concise and logical form. People with this style embody best the stereotype of the ‘aloof academic,’ as they are less inter- ested in people than in abstract concepts and find it more important that a theory has logical soundness than practical value. Accommodators (concrete & active) are the most hands-on learners: They like concrete experience and active experimentation, and they are stimulated by challenging experiences even to the extent of taking risks. They often follow their ‘gut’ feelings rather than logical analysis. No wonder that this learning style is effective in action-oriented careers such as marketing or sales. In for- mal learning situations they like to work with others on active projects and enjoy field work. A brief consideration of Kolb’s classic four-style typology reveals both some of the appeal and some of the weaknesses of the learning styles concept as an ID. As you read through the above summaries of the main facets of Kolb’s four learning styles/types, you may have caught yourself thinking ‘Yes, that’s me!’ This suggests that the concept is tapping into something very real that profoundly resonates with people. However, you may also have found yourself, as we both did, having the ‘that’s me! ’ feeling for more than one (or even all) of the above types; you may have been able to envisage yourself as, say, a diverger in certain contexts or situa- tions, and a converger in others. This suggests a certain lack of clarity or precision. In response to dissatisfaction with the limitations of the four-style typology together with a growing awareness that a learning style “is not a fixed trait but is a dynamic state resulting from continual learning experiences” (Peterson et al., 2014), a new nine-type typology has very recently been proposed (Kolb & Kolb, 2013). A brief description of the proposed nine styles is provided below: Initiating—a person who enjoys leading others and taking action Experiencing—a person who is accepting and sensitive or open to emotions and intuitions Imagining—someone who can create vision through the gathering of infor- mation from diverse sources Reflecting—someone who needs time to absorb and process information Analyzing—a person who is thoughtful and capable of expressing abstract concepts logically and concisely Thinking—a person who tends to enjoy working alone making plans or being involved in rational decision making  Learning Styles and Cognitive Styles 119 Deciding—someone with a clear goal and focused on outcomes Acting—someone committed to a course of action with a reduced concern for risk or potential negative consequences Balancing—someone considering the various possibilities, weighing up the pros and cons of the other style modes The nine-style typology is still very new (see Peterson et al., 2014) and has yet to be subjected to serious academic scrutiny, but what is immediately apparent from our brief outline is that this is an altogether more dynamic conceptualiza- tion of learning styles. At the core of this approach is the notion of learning flex- ibility (Sharma & Kolb, 2010), which concerns the individual’s capacity to adapt preferred styles to contextual demands; people have their own preferred styles but are also able to navigate between these styles. We return to a discussion of the potential of an adaptive conceptualization of learning styles in the conclusion to this chapter. A further issue suggested by our ‘that’s me! ’ reactions mentioned above is one of assessment. When reading the various descriptors there is a tendency to focus on those aspects most applicable to oneself, paying less attention to other aspects that may seem less relevant. This may lead to individuals identifying with a par- ticular style that may not match their learning approach as a whole. Therefore, learning styles need to be operationalized in a measurable way and not merely through descriptors of the style categories; meaningful style assessment requires more than merely matching descriptors with our self-image. Thus, the existence of accurate measuring tools is a prerequisite to the recognition of the validity of various style theories, and this is where cognitive and learning styles so often fall short of the mark. Let us next look at the assessment issue in some detail. Assessing Cognitive and Learning Styles The assessment of cognitive and learning styles is undoubtedly the Achilles’ heel of the concept. In a review of the area, Irvine (2001) stated rather disappointedly that “the enforced conclusion one may have to accept with reluctance is that the means of pursuing, in operational form, the elusive pimpernel of an acceptable measurement protocol for style is not available” (p. 274). He found this all the more disconcerting as in their everyday lives people do not seem to have any trouble identifying various style characteristics. As he pointed out, “The notion of style is so intuitively certain in ordinary people untrammeled by psychologists’ preoccupations with measurement, that professional entertainers make a good living by mimicking styles among the great, the good, the bad, and the ugly” (p. 274). So, if this claim is true and style is relatively easy to capture and imitate, why is it so difficult to measure? When it comes to cognitive and learning styles, currently we know only of two established ways of assessment: either by relying on learners’ own self-reports 120 Learning Styles and Cognitive Styles on how they perceive their cognitive functioning, or by asking learners to per- form mini-information-processing tasks and then making inferences from their performances. Kolb’s Learning Style Inventory (LSI) is a good example of the first type and Riding’s Cognitive Styles Analysis (CSA) of the second. Kolb’s Learning Style Inventory (LSI) The original LSI instrument was a nine-item self-description questionnaire. Each item asked the respondent to rank-order four words in a way that best described their learning style. One word in each item corresponded to one of the four learning modes—concrete experience (e.g., “feeling”), reflective observa- tion (e.g., “watching”), abstract conceptualization (e.g., “thinking”), and active experimentation (e.g., “doing”). The latest version of the LSI is Version 4 (Kolb & Kolb, 2013), which has only very recently been developed to account for the new nine-style typology. However, since this latest version is neither widely used nor have the details of the instrument been extensively published at the time of writ- ing, we focus on the most recent version of the instrument freely available in the public domain, Version 3.1 (Kolb & Kolb, 2005b). This is the version that has had the most practical influence and as such it represents the most appropriate platform from which to discuss this approach to the assessment of learning styles. Version 3.1 of the LSI was extended to 12 items and the actual wording was changed from the single words of the original to a short-statement format, as illustrated in Table 5.4. The initial validation of the LSI scales was carried out with a sample of 1,933 participants. As Kolb (1984) reports, the theoretical assumption that the ‘abstract’ and ‘concrete thinking’ categories were opposite ends of a continuum was borne out by significant negative correlation (-0.57) between the two orientations. TABLE 5.4 Sample items from Kolb’s (2005b) Learning Style Inventory (Version: LSI 3.1) The four statements in both sample items need to be rank-ordered according to how they refer to the respondents. Thus, four marks are to be given to the statement that is most true and one to the one that is least appropriate. When I learn: I like to deal with my feelings I like to watch and listen I like to think about ideas I like to be doing things I learn best from: Observation Personal relationships Rational theories A chance to try out and practice  Learning Styles and Cognitive Styles 121 Similarly, there was also a significant negative correlation (-0.50) between ‘active’ and ‘reflective’ information processing orientations. On the other hand, there was no substantial intercorrelation between the components associated with the two different dimensions. However, others have raised questions about the instrument, with Coffield et al. (2004) concluding that “problems about reliability, validity and the learning cycle continue to dog this model” (p. 70). One particular issue has been test and re-test reliability, with some studies (e.g., Ruble & Stout, 1993; Loo, 1996) finding individuals dramatically changing their learning style upon re-taking the test, and another recurring issue was the test’s limited construct validity (see e.g., Metallidou & Platsidou, 2008). It was largely these criticisms that led to subsequent revisions and refinements of the instrument; Kolb (1999) was able to remedy some of the issues by increasing the number of items, and others have proposed further revisions (see e.g., Manolis, Burns, Assudani, & Chinta, 2013, which proposed a shorter version, the ‘Reduced Learning-Style Inventory’). Even if we leave aside the psychometric issues, a fundamental question still remains: Are the attributes that the scales measure indices of learning styles or something else? Kolb, Boyatzis, and Mainemelis (2001) offered some evidence of the ambiguous nature of this issue because, as they summarized, the main dimensions of the LSI correlate significantly with certain components of the Myers-Briggs Type Indicator (MBTI), which is primarily a personality type inventory, although as was pointed out earlier, various psychological types dis- play a strong link with certain learning styles and therefore the MBTI is often cited when discussing learning styles. This brings us back to the earlier issue that styles appear to be ‘halfway products’ somewhere between personality, intelli- gence, and strategies. We shall come back to this issue at the end of this chapter. A final point we need to consider is how we interpret the results of an instru- ment such as the LSI, and what do we do with the data it provides? The Coffield team’s (2004) review offers a pragmatic and balanced view: When it is used in the simple, straightforward, and open way intended, the LSI usually provides an interesting self-examination and discussion that recognizes the uniqueness, complexity and variability in individual approaches to learning. The danger lies in the reification of learning styles into fixed traits, such that learning styles become stereotypes used to pigeonhole individuals and their behavior. (p. 64) This observation points to a deeper contradiction inherent in the concept of learning styles because “the actual nature of what is being measured is constantly shifting from ‘flexible’ to ‘stable’ ” (Garner, 2000, p. 346). This issue is often dis- cussed under the broader question of the malleability of cognitive/learning styles (see Zhang, Sternberg, & Rayner, 2012), and this question will have a special relevance to the discussion on the practical implications of the concept. 122 Learning Styles and Cognitive Styles Riding’s Cognitive Styles Analysis (CSA) Riding’s CSA (Riding, 1991) represents the other main approach to styles mea- surement available to scholars: It does not utilize the introspective self-report format that the LSI is an example of, but rather it tests respondent performance directly. This instrument focuses on cognitive styles rather than learning styles, which allows it to target a narrower and more precisely definable domain. Another feature of the instrument is that it is computer-based and involves reac- tion time measures for the assessment. The CSA comprises three subtests to assess both ends of the wholist–analytic and verbal–imagery dimensions: Subtest 1, Verbal–Imagery dimension: Students are presented a number of statements (48 in total), one at a time, which require a simple true or false response by pressing a button on the keyboard. Half of the statements are about conceptual categories (e.g., “table and chair are the same type”); the other half describe the appearance of objects (“snow and chalk are the same color”). Half of the statements of each type are true, the other half false. This subtest is based on the assumption that imagers respond more quickly to visual items because they find it easier to represent the infor- mation in terms of visual images, whereas verbalizers are at an advantage with the conceptual items because the conceptual category membership is verbally abstract in nature and cannot be represented in visual form. The computer automatically records the response time to each statement and uses this information to calculate a ratio of verbal response time to visual response time. A low ratio corresponds to a verbalizer and a high ratio to an imager, with the intermediate position being described as bimodal. Because both types of items require reading, factors such as reading speed and ability are inherently controlled for by the calculation of the ratio. Subtest 2, Wholist dimension: Students are presented pairs of complex geo- metrical figures side by side on the screen (a total of 20 pairs) and they have to decide about each pair whether they are identical or not. Wholists are assumed to respond more quickly because their natural tendency to focus on the whole picture corresponds to the task of absorbing the whole shapes. Subtest 3, Analytic dimension: This subtest is similar to the previous one in presenting a pair of geometrical shapes at a time (20 times), but this time the question is whether the first figure, which is a relatively simple geometrical shape (e.g., a square or a triangle), is contained within the second, more complex figure. Analytics, who are more inclined to focus on details, respond more quickly because the task requires the larger shape to be broken down into its constituent parts. Once again, the computer records the response times and calculates the wholist–analytic ratio. Riding and Rayner (1998) emphasized several positive features of the CSA: (a) It is an objective test in the sense that it is objectively scored and the respondents  Learning Styles and Cognitive Styles 123 are not aware of the real focus of the assessment; (b) both ends of the style con- tinuum are assessed, which makes it distinct from measuring abilities; (c) because of the limited and simple language it involves, its use is versatile across age and proficiency groups; and (d) the computerized format creates a context-free char- acter, which allows it to be used across situations and cultures. Furthermore, Riding (2001) reported statistical evidence that the two dimensions are unrelated to one another and show no age or gender differences. What is just as important, the scales appear to be unrelated to intelligence, which supports the fact that the styles measured are not simply subtypes of ability. Finally, although correlations of some magnitude were found between certain personality dimensions and the CSA scales, the overall pattern appeared to point to a model in which physi- ologically based personality sources are independent of cognitive style but are moderated by style in their effect on behavior. The reliability of the CSA was called into question by Peterson, Deary, and Austin (2003, 2007), who compared performance on the original CSA test and a parallel version. They concluded that the test was neither sufficiently reliable nor internally consistent. However, the authors added that when the CSA was doubled in length, the wholist–analytic dimension of cognitive style preference became a more stable and reliable measure. Not surprisingly, Riding (2003) questioned these findings because he claimed that Peterson et al.’s study was not executed properly. Nevertheless, the concerns raised by Peterson et al. led Cof- field et al. (2004) to conclude in their review that “the simplicity and potential value of Riding’s model are not well served by an unreliable instrument” (p. 44). This last comment seems to capture an inherent problem with the concept of styles; when styles are theorized in a parsimonious and comprehensive fashion, they become difficult, perhaps impossible, to measure reliably. This may also be a significant factor behind the relative slowdown in recent activity in this area: Learning styles assessment instruments have shown very little development since 2005. Cognitive and Learning Styles in L2 Studies Given the variability in both the rate of learning and the ultimate level of attain- ment observed among language learners, the field of learning styles—that is, the study of how learners prefer to learn—would seem to be a pertinent area of inquiry for L2 studies. Indeed, over the years, there has been a long-standing research interest in language learning styles, and several instruments have been developed and used to understand the role of learning styles in SLA. However, despite the levels of interest and perceived importance of the concept, hardly any attempt has been made to address the issue of the various conceptual ambiguities and difficulties associated with the notion of learning styles in the psychological literature. This problem has been augmented by the fact that empirical studies conducted on L2 learning styles have typically produced weak, mixed, or at best 124 Learning Styles and Cognitive Styles moderate results; as a consequence, there has been a gradual loss of interest in language learning styles research. In the following discussion, we first address two style concepts adapted from mainstream educational psychology to the field of L2 education—field dependence–independence and sensory preferences—that have received the most L2 research attention (for an exception, see Andreou, Andreou, & Vlachos, 2008, which applied Kolb’s model), followed by an overview of the best-known bat- teries and constructs used to assess language learning styles. Finally, the chapter concludes by looking at the controversial issue as to whether the notion of learn- ing styles has any practical relevance to classroom practitioners. Field Dependence–Independence in L2 Studies The initial momentum in L2 styles research was generated by the conceptualiza- tion of field dependence–independence (FD/I). Psychological research on FD/I was initiated by Herman Witkin over 50 years ago and was originally associated with visual perception: It was noticed that people could be categorized in terms of the degree to which they were dependent on the structure of the prevailing visual field. Some people are highly dependent on this field, which in practical terms means that they cannot see inconspicuous things right in front of their nose—for example, they are hopeless when looking for some small object (such as a nail) dropped on the floor. Field-independent people on the other hand are free—or independent—of the influence of the whole field when they look at the parts and therefore can notice details that their field-dependent counterparts simply cannot ‘see.’ Thus, field-independent people make perfect scouts, for example, as they can notice an enemy’s camouflage against its natural background. Perhaps the best illustration of FD/I comes in the various visual puzzles that appear in magazines or online, in which readers must find figures or shapes concealed within another picture; in fact, this forms the basis for the main assessment instrument for FD/I, the Embedded Figures Test (EFT). The FD/I style distinction, however, is more than a mere perceptional char- acteristic, as it is assumed to affect the individual’s whole behavior in a simi- lar way to Riding’s wholist–analytic style (which is thought to subsume FD/I). Sternberg and Grigorenko (2001) argued that field independence is almost always the preferable style, and indeed, as Johnson, Prior, and Artuso (2000) summa- rized, much of the literature on the construct reports that field independents tend to outperform field dependents on cognitive tasks. This makes intuitive sense because field independents, by definition, are better at focusing on some aspect of an experience or a stimulus, separating it from the background, and analyzing it unaffected by distractions. However, it has also been proposed that when the target of our attention is a complex domain—such as language with its prominent cognitive, affective, and social dimensions—being able to focus on the background, that is, the whole situation, can have its advantages (Chapelle,  Learning Styles and Cognitive Styles 125 1995): Field dependents are more responsive, as they interact with the environ- ment and, thus, tend to have a stronger interpersonal orientation and greater alertness to social cues than field independents. Thus, in L2 studies field dependence may not necessarily be a disadvantage because the accompanying social sensitivity can be a real asset in certain tasks; for example, in Johnson et al.’s (2000) study, the researchers found that field dependents, as opposed to field independents, performed better on L2 tasks that emphasized communicative rather than formal aspects of language proficiency. Other researchers, however, found that field independents had an overall advan- tage in various aspects of SLA (for reviews, see Brown, 2000; Chapelle, 1995; Hoffman, 1997), which could be related to their ability to separate the essential from the inessential, as well as a greater capacity to channel attention selectively and to notice important aspects of language. In a relatively recent review of the literature, Nel (2008) concluded that field-independent (FI) language learners tend to be more successful at deductive tasks, whereas field-dependent (FD) lan- guage learners perform better at inductive tasks. In practical terms this suggests that the FI individual benefits from the way he or she processes information but tends to avoid situations in which language is actually going to be used for com- munication. FD individuals, while comfortable and sensitive in communication situations, tend not to be effective information processors, and so, although pro- vided with more information to work with, will exploit it less. From this, one can infer that FI individuals should do better on non-communicative, more cere- bral tests, while FD individuals should excel in more communicative situations, when what is assessed is language use rather than language-like use. We should note, however, that this clear-cut and seemingly straightfor- ward, logical pattern is partly the result of speculation and wishful thinking, because the actual research results are far from being strong, and are often non- significant or conflicting (cf. Ellis, N. C., 1994). This led Griffiths and Sheen (1992) to dismiss the whole line of FD/I research in SLA, claiming that “field dependence/independence does not have, and never has had, any relevance for second-language learning” (p. 131). A final issue concerning FD/I is to what extent this cognitive style is inde- pendent of other cognitive factors. Over the years, studies have consistently reported high correlations with verbal and performance aspects of intelligence and, consequently, Sternberg and Grigorenko’s (2001) summary was rather grim: “Thus, the preponderance of evidence at this point suggests that field independence is tantamount to f luid intelligence” (p. 7). This correlation, how- ever, might be because of measurement deficiency: The Embedded Figures Test (EFT) and its group version, the GEFT, are paper-and-pencil instruments that require students to attempt to discern simple geometric figures from more com- plicated patterns. As Riding (2000a) argued, it was assumed in these tests that FI individuals would be able to complete tasks more quickly than FD ones; however, the tests do not include any subtests on which the FD individuals are 126 Learning Styles and Cognitive Styles likely to outperform the FI ones, and therefore the overall test score is more like an ability score, ranging from bad to good, than a bipolar cognitive style score. Thus, as with other cognitive and learning styles, the validity of a style concept and the psychometric qualities of the instrument that measures this concept are inextricably bound. Sensory Preferences The learning style dimension that most language teachers, and even many lan- guage students, would be familiar with is the categorization of sensory preferences into ‘visual,’ ‘auditory,’ ‘kinesthetic,’ and sometimes ‘tactile’ types (often referred to as VAKT). This dimension concerns the perceptual modes or learning chan- nels through which students take in information. Let us look at the preference types: Visual learners outnumber all the other three groups; Oxford (1995) reported that in her experience as many as 50% to 80% of people in any class would say they are predominantly visual. As the term suggests, these learners absorb information most effectively if it is provided through the visual channel. Thus, they tend to prefer reading tasks and often use colorful highlight- ing schemes to make certain information visually more salient. In general, visual learners like visual stimulation such as films, and if some large chunk of information is presented orally (e.g., in a lecture), their understanding is considerably enhanced by a handout and various visual aids, as well as by taking extensive notes. Auditory learners use most effectively auditory input such as lectures. They also like to ‘talk the material through’ by engaging in discussions and group work. They benefit from written passages to be read out loud and they often find that reciting out loud what they want to remember (even telephone numbers or dates) is helpful. Kinesthetic and tactile learners are often grouped together under the ‘haptic’ style category and this is understandable because the two style preferences are somewhat related although not identical. The kinesthetic style refers to learning most effectively through complete body experience (e.g., whole- body movement), whereas tactile learners like a hands-on, touching learning approach. The key issue for the former group is movement, while for the latter the manipulation of objects. Kinesthetic learners thus require frequent breaks or else they become fidgety—sitting motionless for hours is a real challenge for them. They often find that walking around while trying to memorize something helps. Tactile learners enjoy making posters, collages, and other types of visuals, and building models; they also happily engage in creating various forms of artwork. For them conducting a lab experiment may be a real treat.  Learning Styles and Cognitive Styles 127 The different sensory preferences do not exclude each other. For example, successful learners often use both visual and auditory input, but they are said to display slight preferences, or modality strengths, one way or the other. As students grow older, those with mixed modality strengths are believed to have a decidedly better chance of success than do those with a single modality strength because they can process information in whatever way it is presented (Kinsella, 1995). The notion of sensory preferences encapsulates so much of the debate surround- ing learning styles. The observation that sensory preferences affect our learning is an intuitively appealing and personalized explanation of human behavior, but the actual evidence that such preferences impact learning is threadbare (Willingham, 2005). In this respect some interesting results have been reported by Dörnyei and Chan (2013): As they argued, while in the learning styles literature visual and auditory style preferences have typically been discussed as forming a visual– auditory continuum, the actual measurement of these styles usually involves sepa- rate numeric rating scales for both the visual and the audio components (rather than a comparison or a forced choice between them). Therefore, these scales are measured by graded response options (e.g., marking one’s response on a 1–5 scale), thereby not so much indicating preference as strength (e.g., marking “5” indicates a stronger relevance than marking “3”). Consequently, a high score on these scales indicates, in effect, highly developed sensory processing skills in L2 learning as reported by the student. This not only explains the common observation in the past that learners can be equally high or low in both style dimensions, but it also links these style measures to the imagery aspect of motivation as conceptualized by the L2 Motivational Self System (see Chapter 4). Indeed, Dörnyei and Chan present significant correlations between sensory preferences and both the ideal and ought-to self-guides, suggesting that the link between sensory styles and learning behavior might be mediated by motivation. Assessing Language Learning Styles There have been a number of published instruments available for teachers and researchers to measure L2 learning styles. They all follow a self-report format in which respondents are to indicate their answers by marking one of the options on a rating scale. The tests vary in how much reliability and validity data have been reported about them by the authors, but it is fair to say that most of them have been developed for practical rather than research purposes, that is, to raise language learners’ awareness of style issues in general and of their own style pref- erences in particular. Thus, these batteries have normally not been fine-tuned for scientific measurement purposes by submitting them to the kind of rigorous standardization process that is a requirement in psychology for an instrument to become admissible. The following section presents a sample of the best-known tests. Describing their components also offers a good opportunity for introduc- ing the various style dimensions they cover. 128 Learning Styles and Cognitive Styles Perceptual Learning Style Preference Questionnaire (PLSPQ) Joy Reid’s (1995) Perceptual Learning Style Preference Questionnaire (PLSPQ; originally developed in 1984) was the first learning style measure widely known in the L2 field. Although the author is an L2 researcher and the instrument has been used with L2 learners, it is in fact not L2-specific, as the items do not mention any subject matter. Based loosely on the VAKT model, it consists of 30 randomly ordered statements for six learning style preferences: visual, auditory, kinesthetic, tactile, group learning, and individual learning. It uses 5-point Likert-scale items ranging from ‘strongly agree’ to ‘strongly disagree,’ focusing on behavioral preferences (e.g., “I learn more by reading textbooks than by listening to oth- ers.”). The instrument is very user-friendly, with an accompanying self-scoring sheet and a short explanation of learning style preferences that also contains prac- tical suggestions for learners. Table 5.5 presents a sample item from each scale. Style Analysis Survey (SAS) Rebecca Oxford’s (1993; Reid, 1995) Style Analysis Survey (SAS) is similar to the PLSPQ in that although it has been devised by an L2 expert and has primarily been used with L2 learners, the items themselves are not subject-specific. The similari- ties do not end here: Both tests consist of five parts, but the SAS is more complex and with its 110 items is considerably longer than the PLSPQ. Section 1 of the SAS targets sensory preferences similarly to the PLSPQ, but the other four sections focus on other established personality/style characteristics: extraversion vs. introversion, intuitive vs. concrete/sequential, closure-oriented vs. open, global vs. analytic. Table 5.6 provides a brief description and a sample item for each style dimension. Respondents give their answers on 4-point rating scales with ‘never’ and ‘always’ as the two poles. The SAS is also a user-friendly test, with a self-scoring sheet, expla- nations about the results, and some practical tips and suggestions. TABLE 5.5 Sample items from Joy Reid’s Perceptual Learning Style Preference Questionnaire (Reid, 1995, pp. 202–207) Visual preference I learn more by reading textbooks than by listening to others. Auditory preference I learn better in class when the teacher gives a lecture. Kinesthetic preference When I do things in class, I learn better. Tactile preference I enjoy making something for a class project. Group preference I learn more when I study with a group. Individual preference When I study alone, I remember things better.  Learning Styles and Cognitive Styles 129 TABLE 5.6 Description of Oxford’s (1993) Style Analysis Survey I. How I use my physical senses to study or work (30 items) Visual, auditory, This section is similar to the corresponding parts of the PLSPQ. hands-on II. How I deal with other people (20 items) Extroverted Turning outward and gaining energy from the external world. E.g., “Wherever I go, I develop personal contacts.” Introverted Turning inward for our sense of wholeness and self-esteem. E.g., “In a large group, I tend to keep silent.” III. How I handle possibilities (20 items) Intuitive-random Thinking in an abstract, future-oriented way, willing to rely on hunches, inspiration, and imagination for perceiving reality. E.g., “I have a vivid imagination.” Concrete Being concerned with facts and preferring them to be presented sequential in a step-by-step, organized fashion. E.g., “I behave in a down-to-earth way.” IV. How I approach tasks (20 items) Closure-oriented Having a need for clarity and preferring to plan ahead and follow instructions without any improvisation. E.g., “I make lists of things I need to do.” Open Preferring spontaneity, flexible situations without concern for deadlines. E.g., “I like to just let things happen, not plan them.” V. How I deal with ideas (20 items) Global Focusing on the big picture and following instincts or guesswork in distilling the main principles of a certain material. E.g., “I can summarize information rather easily.” Analytic Preferring to work our way through the material systematically and breaking units apart to understand them. E.g., “I use logical analysis to solve problems.” The SAS proved to be a popular and widely used instrument, particularly in classrooms. It also influenced the development of other instruments, such as Cohen, Oxford, and Chi’s (2001) Learning Style Survey (LSS), which was, in effect, an expansion and refinement of the SAS. The SAS, and the various instru- ments that followed in its wake, were determinedly classroom-oriented and do a satisfactory job of raising learner awareness of learning styles. The Ehrman and Leaver Learning Styles Questionnaire In contrast to the SAS approach, the E&L Construct, as Madeline Ehrman and Betty Lou Leaver (2003; Ehrman, 2001) named their system, represented an attempt to reconceptualize cognitive styles in language learning. It is similar to 130 Learning Styles and Cognitive Styles Riding’s theory in that it reorganizes a number of established style dimensions under a new, comprehensive, and parsimonious superordinate construct. How- ever, unlike Riding’s taxonomy, here only one superordinate style dimension—or metastyle—is provided, with the two poles labeled ectasis and synopsis. The main difference between the two extremes is that an ectenic learner wants or needs conscious control over the learning process, whereas a synoptic learner leaves more to preconscious or unconscious processing (see Figure 5.1 for a summary). The complete system is made up of 10 sub-dimensions, and Ehrman and Leaver (2003) provided a detailed rationale and theoretical explanation of the E&L Construct in which they point out that all 10 subscales of the E&L Con- struct represent established style dimensions with a body of relevant literature available for each, although one dichotomy, the analogue–digital dimension, had not been applied to learning contexts before. Let us briefly consider each subscale (for more details, see Ehrman & Leaver, 2003; Leaver et al., 2005): Field dependent–independent and field sensitive–insensitive (2 subscales): Field dependence–independence has been discussed in a separate section before; although the terms (in)dependence and (in)sensitivity have often been used in the literature in an interchangeable manner, Ehrman and Leaver distinguish them to the extent that they constitute two different scales in the overall construct. Based on Ehrman (1998), field dependence–independence refers to the preference for selection and prioritization vs. treating the whole con- text as the same, whereas field sensitivity–insensitivity concerns the prefer- ence for considering materials in a situated manner and being aware of their position in their broader context. Thus, field sensitivity relates to foreground and background together whereas field dependence treats the foreground and the background as the same. Field-sensitive learners prefer to address material as part of the context in contrast to their field-insensitive counter- parts, who make little or no use of the context. Random (nonlinear) vs. sequential (linear): This dimension relates to how the learner processes information. Random learners follow their own, internally developed and idiosyncratic order of processing (which may seem random to others), whereas sequential learners prefer a step-by-step, externally provided order of processing (such as the units in a syllabus). Global–particular: This dimension is well encapsulated by the top-down vs. bottom-up processing metaphor. Inductive–deductive: Inductive learners start with the details and facts, then form hypotheses, and finally test them; deductive learners start out with rules or theories and then try to apply them to examples. Synthetic–analytic: Synthetic learners like to use pieces to build new wholes, whereas analytic students like to disassemble wholes into parts to understand their componential structure.  Learning Styles and Cognitive Styles 131 Analogue–digital: Analogue learners prefer to use metaphors, analogies, and conceptual links among units and their meanings, whereas digital learners take a more surface approach, characterized by a literal and logical under- standing of what they can hear or see. Concrete–abstract: Concrete learners prefer a relationship with direct experi- ence to the extent of sensory contact, whereas abstract learners may have more interest in the system underlying language than in the actual language of communication. Leveling–sharpening: This dimension concerns how people perceive, store, and retrieve information. Levelers often blur things together and form a general- ized image, whereas sharpeners notice small differences and store them as salient attributes in their memories. Impulsive–reflective: Impulsive learners tend to respond rapidly, often acting on gut, whereas reflective learners prefer to think things through before they respond. Ehrman and Leaver emphasized that this is a real style dimension— rather than an ability continuum in which impulsive is inefficient and reflec- tive efficient—in the sense that both poles can be beneficial or dysfunctional. The E&L Construct was operationalized by the creation of the Ehrman & Leaver Learning Style Questionnaire. This instrument contains 30 items using a 9-point semantic differential scale format and provides a rich set of data about an individual in the form of an emerging profile, which has the advantage both of generality and specificity. Table 5.7 presents 10 sample items from the test and Figure 5.1 contains a sample scoring grid. As Ehrman and Leaver (2003) explained, the synoptic–ectenic construct level can be used when a learner has a clear set of preferences tending to the right or the left of the chart (as is the case in the sample grid), which allows for a concise description. At the same time, the profile can also yield a more elaborate portrayal of an individual through the interplay of the 10 subscales. However, because of the intercorrelation of the subscales, the multiplicity of profiles still falls within the same relatively standardized system. At the time of writing the original version of this chapter, the E&L Con- struct, and the associated questionnaire, represented a highly promising attempt to conceptualize language learning styles in a systematic and principled fashion. Unfortunately, perhaps because of the limited availability of the instrument or because of the complexity of interpreting the resultant profile, the instrument has not been widely used in the years since its publication. As Ehrman (personal communication, 13 October 2014) explains, one reason for not placing the test in the public domain has been a concern about publishing it without protec- tion against misuse, as “there’s something of a clinical element to getting the most out of it (discussion of hypotheses and apparent contradictions with the respondent).” TABLE 5.7 Sample items from the Ehrman & Leaver Learning Style Questionnaire 1. When I work with new material in context, I don’t usually get much from the in stories or articles or at least sentences, I context unless I pay close attention to often pick up new words, ideas, etc. that way, what I’m doing. I certainly wouldn’t without planning in advance. You could say I describe myself as someone who learns make a lot of use of a floodlight to learn. by osmosis. It usually has to be out there in black and white. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 2. When working with new material with When there is a lot of information that additional subject matter around it, I comes with what I need to learn, it’s comfortably find and use what is most hard to tell what’s most important. It all important. I also like out-of-context material seems to fall together sometimes, and it’s like grammar rules. You could say I make a lot hard work to sort things out. of use of a spotlight to learn. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 3. I like to reduce differences and look for I like to explore differences and similarities. I notice mostly how things are disparities among things and tend to similar, and I level out differences. notice them quickly. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 4. I tend to be most aware of and interested in I notice specifics and details quickly; I the big picture; I notice the forest before the tend to be aware of the trees before the trees; I start with the main points and work forest. I begin with the details to work down to the details. up to the main points. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 5. I react quickly, often acting or speaking I tend to think about things before I do without thinking about it. or say them. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 6. I understand best by assembling what I understand best by disassembly of I’m learning into a whole, synthesizing learning into its component parts, information. analyzing information. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 7. I tend to learn things through metaphors and I like things that can be counted and associations with other things. I often learn that say what they mean directly. I take through stories or example cases. things at face value. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 8. To learn, I like to interact with the world I like to learn through concepts and and learn through application of knowledge, ideas and from formal renditions of especially when I can touch, see, or hear it. knowledge like theories and models. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 (Continued) TABLE 5.7 (Continued) 9. I learn best when I can work out for myself I learn best when there is a sequence the best sequence to use, even if it’s different of steps provided, so I can do things in from the one in the book or lesson. order. Textbooks and lesson plans really help me. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 10. When I learn, I mostly start with examples or When I learn, I mostly start with rules my experience and make generalizations or and generalizations and apply them to rules. my experience to learn. Most like this ___ ___ ___ ___ ___ ___ ___ ___ ___ Most like this 1 2 3 4 5 6 7 8 9 1: Field (in)sensitivity, 6: Synthetic–analytic, 2: Field (in)dependence, 7: Analogue–digital, 3: Leveling–sharpening, 8: Concrete–abstract, 4: Global–particular, 9: Random–sequential, 5: Impulsive–reflective, 10: Inductive–deductive. Name: XY ID Code: 0000 Synoptic Ectenic 1 2 3 4 5 6 7 8 9 Field Sensitive X Field Insens. Field Indep. X Field Dep. Random X Sequential Global X Particular Inductive X Deductive Synthetic X Analytic Analogue X Digital Concrete X Abstract Leveling X Sharpening Impulsive X Reflective 5 4 3 2 1 2 3 4 5 FIGURE 5.1 Sample scoring grid for the E&L Construct (Ehrman & Leaver, 2003) 134 Learning Styles and Cognitive Styles Griffiths’s Inventory of Language Learning Styles (ILLS) As mentioned earlier, there has been little recent development of instruments to assess language learning styles, which is a ref lection of how scholarly inter- est in the area has waned. One exception to this generalization has been Carol Griffiths’s (2012) Inventory of Language Learning Styles (ILLS), and taking a closer look at it will illustrate the shifting attitudes toward the concept of learning styles within L2 studies. What is revealing about this inventory is that it makes no claims of theoretical purity, nor does it seek to reconceptualize language learning styles in any grand fashion. Instead, this is a down-to-earth, pragmatic instrument with a singularly pedagogic focus. It brings together a mixed selection of items from various assessment instruments, from both inside and outside L2 studies, in a compact and easy-to-administer format. In doing so, the ILLS seems to suggest a need to take stock as well as a sense of the end of the line for a serious research-based approach to the study of styles in L2 learning. This instrument implies an acknowledgement that such instruments (which measure learning styles) need to address the immediate, practical con- cerns of the classroom. This theme of a general scaling down of ambition is one we return to below when we look at some of the classroom applications of learning styles. Practical Implications The discussion so far has revealed the concepts of cognitive and learning styles to be somewhat vague and elusive from both theoretical and research perspec- tives. However, one may ask whether the potential practical value of styles may compensate for the theoretical limitations. That is, can the concept be used in any way to promote the effectiveness of learning? The answer that the field of education has given is a qualified yes; in fact, Coffield et al. (2004) point out that much of the styles-related activity and development has been driven by the needs of practitioners rather than by learning theorists. The essence of the learn- ing styles hypothesis is that learning can become more effective when instruc- tion is tailored to meet individual needs in a way that it takes into account the individual’s learning style. As we discussed in the introduction to this chapter, this is an attractive and intuitively appealing notion for all involved in learning, as it promises not only improved learning outcomes but also a more pleasant learning experience. Unfortunately, intuitively appealing notions do not always stand up to rigorous scientific investigation, and in an exhaustive review of the learning styles literature, Pashler et al. (2009, p. 105) surmise that “there is no adequate evidence base to justify incorporating learning styles assessments into general educational practice.” However, the authors also emphasize that given the lack of methodologically sound studies of learning styles, it would be an error to conclude that all possible educational avenues in this respect have been  Learning Styles and Cognitive Styles 135 tested and found wanting. Bearing these thoughts in mind, let us consider some of the positive contributions that an understanding of the concept can poten- tially offer. In her book on understanding second language learning difficulties, Ehrman (1996) justified the extensive treatment of learning styles by claiming that “learn- ing style mismatches are at the root of many learning difficulties” (p. 50). What kind of mismatches are we talking about? We can conceive of at least six types of possible style conflict: 1. Mismatch between the student’s learning style and the teacher’s teaching style, a conflict that has been dramatically termed a ‘style war’ by Oxford et al. (1991). 2. Mismatch between the student’s learning style and the syllabus, for example when the latter does not cover grammar systematically, although analytic learners would need that. 3. Mismatch between the student’s learning style and the language task, for example when a visual student participates in a task that involves receiving auditory input. 4. Mismatch between the student’s learning style and his or her beliefs about learning, for example when an analysis-oriented learner believes that rote learning is the most effective learning method (whereas that method would suit a memory-oriented learner better). 5. Mismatch between the student’s learning style and the learning strategies applied, for example when a field-independent learner tries to apply social strategies, or a global learner uses bottom-up reading strategies. 6. We can even conceive of a mismatch between the student’s learning style and his or her abilities, for example when an ectenic learner has underdevel- oped grammatical sensitivity. So, most experts would agree that some sort of style harmony might be ben- eficial in many respects for teachers and learners alike. The question, then, is whether this is feasible. There are some reasons to doubt the viability of establishing some form of style-based instruction that may facilitate this har- mony. One significant factor that applies to many of the constructs discussed in this book is a simple lack of resources; in many educational contexts, lan- guage instruction takes the form of large classes, and teachers are limited in how they are able to personalize language learning in a way that takes an indi- vidual learner’s styles into account. A further issue concerns most classroom practitioners’ ill-preparedness to deal with styles in a meaningful way: In an ideal world, teacher training would include a more prominent psychological component, but, since this is generally not the case, requiring teachers to nego- tiate the complicated and fragmented area of learning styles, with its prolif- eration of often overlapping terminology, may lead to confusion. Thus, in a 136 Learning Styles and Cognitive Styles refreshingly down-to-earth analysis of the possible educational applications of learning styles, Yates (2000) warns us that the idea that we can create instruc- tional programs or plan curriculum variations to match our students’ cognitive style characteristics reflects a “visionary position that, unfortunately, is neither viable nor justified. It is unrealistic for a classroom teacher to classify students into cognitive style categories to be used to prescribe differential educational experience” (p. 359). Not prescribing “differential educational experiences” does not mean, however, that an awareness of the style issue may not be beneficial. In this respect Gregersen and MacIntyre (2014, pp. 180–183) offer a practical set of guidelines in the form of five basic ‘principles’ (summarized in Table 5.8). These principles provide a sturdy and realistic pedagogic platform for incor- porating an understanding of learning styles into classroom practice: a plat- form based upon an awareness of and sensitivity to the various styles that exist in a classroom, the provision of a variety of instructional styles, and the need for both teachers and learners to ref lect on their own style preferences. A fur- ther common-sense suggestion comes from Peacock (2001), who recommends a greater role for learners in planning lessons and tasks as a way of minimizing style conf licts. In a recent review of the educational applications of cognitive styles, Kozhevnikov, Evans, and Kosslyn (2014) point out that research over recent decades has highlighted the importance of helping students to become sensi- tive and proficient in a variety of strategies and approaches in tackling different educational tasks, and therefore there has been an increased focus in educa- tional research on helping students “to self-regulate their learning and flexibly switch between styles, according to situational requirements” (p. 12). Indeed, “style flexibility” has recently been foregrounded in educational psychology as an emerging new theme of significance, along with the recommendation that teachers should help students to develop appropriate cognitive styles in relation to the needs of the tasks. This approach reverses, in effect, the original recom- mendation of adjusting the instruction to the students’ style characteristics by focusing on how students can modify their own style preferences to suit different learning contexts and tasks. Gregersen and MacIntyre’s (2014) principles fully embrace the spirit of this move. Finally, although styles may have limited applications in mainstream class- rooms, what about other forms of instruction? One of the fastest growing areas of education is online learning, and since this implies a different learning dynamic to that of face-to-face instruction, there has been some scholarly interest in how established concepts from the styles literature may be employed to enhance the provision of online education (e.g., Richmond & Cummings, 2005). Indeed, Kozhevnikov et al. (2014) point out that there is growing backing for the idea that online learning environments can support multiple cognitive styles, pro- vided that the relevant technologies are available. This being the case, style  Learning Styles and Cognitive Styles 137 TABLE 5.8 Gregersen and MacIntyre’s (2014) five principles for the practical classroom application of styles Principle 1: Effective teachers are aware of their own instructional styles. Teachers often teach in the way they were taught, or the way that they learned best. A teacher’s own learning experiences are a major influence on a whole range of decisions, from the design of specific learning tasks to the overall teaching style. Of course, it is impractical for teachers to match their teaching style to the individual preferences of everybody in the classroom, but teachers need to develop an awareness of their own style and the potential for mismatch. Principle 2: Self-aware learners identify their preferred approaches to language learning for themselves and for their teachers. Perhaps the most direct, and important, application of learning styles is in developing learner awareness of their own learning styles. This may be achieved by getting students to take a learning style questionnaire and by discussing the results. Understanding their own learning styles can make learning more effective, not least because this knowledge enables teachers to orient their teaching to the styles of their learners and to offer more stylistic variety (Nel, 2008). Principle 3: Through a “mixed and many” approach, teachers and learners together can explore ways to balance their styles. In any situation that involves a group of people learning together over an extended period of time, style conflicts are bound to occur. These style conflicts can be mitigated by teachers varying the style of instruction. In practice, this means a more balanced mixture of instructional input, with the materials presented visually as well as verbally, and reinforced through writing, drawing, or speaking activities. Principle 4: By agreeing to occasionally “stretch” and sometimes “match,” teachers and learners together can resolve learning style conflicts. So far our discussion has concentrated on the benefits of avoiding style mismatches and matching instruction to the preferred styles of the learner. However, there are occasions when these style preferences represent nothing more than familiar ‘comfort zones’ (Ehrman, 1996). There are times when learners can benefit from exposure to unfamiliar styles, from operating outside their preferred styles, a phenomenon that is often referred to as style stretching. Principle 5: Reflective learners consider how their beliefs, strategies, and abilities connect to their individual learning styles. In addition to the possibility of external style mismatches, there is also a risk of internal conflict. For example, an individual learner may hold certain beliefs about the nature of language learning but find these beliefs conflict with a preferred learning style. Learners may benefit from opportunities to reflect upon these internal conflicts and to discuss them with both peers and teachers. flexibility becomes a salient issue here because of the belief that students can adapt their styles to the requirements of