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Section 3 Genetic and other constitutionally based factors LSRC reference page 12/13 Introduction Widespread beliefs that people are born with various element-based temperaments, astrologically determined characteristics, or personal qualities associated with right- or left-handedness have for centuries been common in many cultures. Not dissimilar beliefs are held by those theorists of cognitive and/or learning style who claim or assume that styles are fixed, or at least are very difficult to change. To defend these beliefs, theorists refer to genetically influenced personality traits, or to the dominance of particular sensory or perceptual channels, or to the dominance of certain functions linked with the left or right halves of the brain. For example, Rita Dunn argues that learning style is a ‘biologically and developmentally imposed set of characteristics that make the same teaching method wonderful for some and terrible for others’ (Dunn and Griggs 1998, 3). The emphasis she places on ‘matching’ as an instructional technique derives from her belief that the possibility of changing each individual’s ability is limited. According to Rita Dunn, ‘three-fifths of style is biologically imposed’ (1990b, 15). She differentiates between environmental and physical elements as more fixed, and the emotional and ‘sociological’ factors as more open to change (Dunn 2001a, 16). Genetics All arguments for the genetic determination of learning styles are necessarily based on analogy, since no studies of learning styles in identical and non-identical twins have been carried out, and there are no DNA studies in which learning style genes have been identified. This contrasts with the strong evidence for genetic influences on aspects of cognitive ability and personality. It is generally accepted that genetic influences on personality traits are somewhat weaker than on cognitive abilities (Loehlin 1992), although this is less clear when the effects of shared environment are taken into account (Pederson and Lichtenstein 1997). Pederson, Plomin and McClearn (1994) found substantial and broadly similar genetic influences on verbal abilities, spatial abilities and perceptual speed, concluding that genetic factors influence the development of specific cognitive abilities as well as, and independently of, general cognitive ability (g). However, twin-study researchers have always looked at ability factors separately, rather than in combination, in terms of relative strength and weakness. They have not, for example, addressed the possible genetic basis of visual-verbal differences in ability or visual-auditory differences in imagery which some theorists see as the constitutional basis of cognitive styles. According to Loehlin (1992), the proportion of non-inherited variation in the personality traits of agreeableness, conscientiousness, extraversion, neuroticism and openness to experience is estimated to range from 54% for ‘openness’ to 72% for ‘conscientiousness’. Extraversion lies somewhere near the middle of this range, but the estimate for the trait of impulsivity is high, at 79%. To contrast with this, we have the finding of Rushton et al. (1986) that positive social behaviour in adults is subject to strong genetic influences, with only 30% of the variation in empathy being unaccounted for. This finding appears to contradict Rita Dunn’s belief that emotional and social aspects of behaviour are more open to change than many others. The implications of the above findings are as follows. Learning environments have a considerable influence on the development of cognitive skills and abilities. Statements about the biological basis of learning styles have no direct empirical support. There are no cognitive characteristics or personal qualities which are so strongly determined by the genes that they could explain the supposedly fixed nature of any cognitive styles dependent on them. As impulsivity is highly modifiable, it is unwise to use it as a general stylistic label. ‘People-oriented’ learning style and motivational style preferences may be relatively hard to modify. Modality-specific processing There is substantial evidence for the existence of modality-specific strengths and weaknesses (for example in visual, auditory or kinaesthetic processing) in people with various types of learning difficulty (Rourke et al. 2002). However, it has not been established that matching instruction to individual sensory or perceptual strengths and weaknesses is more effective than designing instruction to include, for all learners, content-appropriate forms of presentation and response, which may or may not be multi-sensory. Indeed, Constantinidou and Baker (2002) found that pictorial presentation was advantageous for all adults tested in a simple item-recall task, irrespective of a high or low learning-style preference for imagery, and was especially advantageous for those with a strong preference for verbal processing.
The popular appeal of the notion that since many people find it hard to concentrate on a spoken presentation for more than a few minutes, the presenters should use other forms of input to convey complex concepts does not mean that it is possible to use bodily movements and the sense of touch to convey the same material. Certainly there is value in combining text and graphics and in using video clips in many kinds of teaching and learning, but decisions about the forms in which meaning is represented are probably best made with all learners and the nature of the subject in mind, rather than trying to devise methods to suit vaguely expressed individual preferences. The modality-preference component of the Dunn and Dunn model (among others) begs many questions, not least whether the important part of underlining or taking notes is that movement of the fingers is involved; or whether the important part of dramatising historical events lies in the gross motor coordination required when standing rather than sitting. Similarly, reading is not just a visual process, especially when the imagination is engaged in exploring and expanding new meanings. More research attention has been given to possible fixed differences between verbal and visual processing than to the intelligent use of both kinds of processing. This very often involves flexible and fluent switching between thoughts expressed in language and those expressed in various forms of imagery, while searching for meaning or for a solution or decision. Similarly, little attention has been given to finding ways of developing such fluency and flexibility in specific contexts. Nevertheless, there is a substantial body of research which points to the instructional value of using multiple representations and specific devices such as graphic organisers and ‘manipulatives’ (things that can be handled). For example, Marzano (1998) found mean effect sizes of 1.24 for the graphic representation of knowledge (based on 43 studies) and 0.89 for the use of manipulatives (based on 236 studies). If such impressive learning gains are obtainable from the general (ie not personally tailored) use of such methods, it is unlikely that basing individualised instruction on modality-specific learning styles will add further value. Cerebral hemispheres It has been known for a very long time that one cerebral hemisphere (usually, but not always, the left) is more specialised than the other for speech and language and that various non-verbal functions (including face recognition) are impaired when the opposite hemisphere is damaged. Many attempts have been made to establish the multifaceted nature of hemispheric differences, but we still know little about how the two halves of the brain function differently, yet work together. New imaging and recording techniques produce prettier pictures than the electroencephalographic (EEG) recordings of 50 years ago, but understanding has advanced more slowly. To a detached observer, a great deal of neuroscience resembles trying to understand a computer by mapping the location of its components. However, there is an emerging consensus that both hemispheres are usually involved even in simple activities, not to mention complex behaviour like communication. Theories of cognitive style which make reference to ‘hemisphericity’ usually do so at a very general level and fail to ask fundamental questions about the possible origins and functions of stylistic differences. Although some authors refer to Geschwind and Galaburda’s (1987) testosterone-exposure hypothesis or to Springer and Deutsch’s (1989) interpretation of split-brain research, we have not been able to find any developmental or longitudinal studies of cognitive or learning styles with a biological or neuropsychological focus, nor a single study of the heritability of ‘hemisphere-based’ cognitive styles. Yet a number of interesting findings and theories have been published in recent years which may influence our conceptions of how cognitive style is linked to brain function. For example, Gevins and Smith (2000) report that different areas and sides of the brain become active during a specific task, depending on ability level and on individual differences in relative verbal and non-verbal intelligence. Burnand (2002) goes much further, summarising the evidence for his far-reaching ‘problem theory’, which links infant strategies to hemispheric specialisation in adults. Burnand cites Wittling (1996) for neurophysiological evidence of pathways that mainly serve different hemispheres. According to Burnand, the left hemisphere is most concerned with producing effects which may lead to rewards, enhancing a sense of freedom and self-efficacy. The neural circuitry mediating this is the dopamine-driven Behaviour Activation System (BAS) (Gray 1973). The right hemisphere is most concerned with responding to novel stimuli by reducing uncertainty about the environment and thereby inducing a feeling of security. In this case, the neurotransmitters are serotonin and non-adrenalin and the system is Gray’s Behavioural Inhibition System (BIS). These two systems (BAS and BIS) feature in Jackson’s model of learning styles (2002), underlying the initiator and reasoner styles respectively.
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Page 5 and 6: LSRC reference Contents 1 9 13 37 4
Page 7 and 8: LSRC reference Figures and tables L
Page 9 and 10: LSRC reference Acknowledgements The
Page 11 and 12: Section 1 A systematic review of le
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Page 19 and 20: Figure 3 Vermunt’s model of learn
Page 21: We devised this classificatory syst
Page 25 and 26: Figure 5 Gregorc’s four-channel l
Page 27 and 28: From the evidence available, we con
Page 29 and 30: Table 1 Gregorc’s Mind Styles Mod
Page 31 and 32: Table 2 Variables and factors in th
Page 33 and 34: The measures Over 25 years, Dunn an
Page 35 and 36: Table 4 Percentages of respondents
Page 37 and 38: The BES instrument has elements for
Page 39 and 40: Shwery (1994) also questioned aspec
Page 41 and 42: However, the notion of ‘gifted’
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Page 45 and 46: Table 6 Dunn and Dunn’s model and
Page 47 and 48: The most influential member of the
Page 49 and 50: Table 9 Studies of the interaction
Page 51 and 52: Riding and Rayner (1998) do not def
Page 53 and 54: Implications for pedagogy Riding (2
Page 55 and 56: Section 5 Stable personality type L
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Page 69 and 70: Section 6 Flexibly stable learning
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Section 7 Learning approaches and s
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Section 8 Implications for pedagogy
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Our recommendation in favour of inc
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As for the research evidence in fav
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Learning styles in practice: labell
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Table 44 13 learning-styles models
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It may be argued that it is importa
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Appendix 3 Glossary of terms LSRC r
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