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CHAPTER 2. Literature Review and Research Problem

2.3 Nature of the learner and skill bases

2.3.3 Cognitive/learning styles, preferences and

There are conflicting views on the value of accommodating individual differences in learning in the development of instruction. Defining individual differences is problematic as a variety of constructs have been formulated such as

cognitive styles, learning styles, learning preferences and learning approaches. It is not the intent of this section to debate or argue for/against any of these views but to offer clarification on these theories. The criticism towards learning styles has mainly centred around the matching of instructional methods and assessments to different individual learning or cognitive styles based on inconclusive evidence that a relationship exists to affect cognitive processing. This controversial issue was highlighted long ago by Cronbach and Snow (1977, p. 492) after extensively reviewing Aptitude Treatment Interaction (ATI), who stated that, “No Aptitude x Treatment interactions are so well confirmed that they can be used directly as guides to instruction”. More recently, the conclusion of Cronbach and Snow was echoed by Clark (2010) and Pashler, McDaniel, Rohrer and Bjork (2008) who do not dispute the existence of learning aptitudes, or indeed learning styles, but maintain that insufficient evidence exists to confirm their interaction with different types of

information. The cost benefits of implementing the results of research by proponents of learning styles, many of which have stemmed mainly from experimental research and review of other empirical studies, can be summarised in the follow statement (Pashler, McDaniel, Rohrer, & Bjork, 2008):

…there is no adequate evidence base to justify incorporating learning styles assessments into general educational practice. Thus, limited education resources would better be devoted to adopting other educational practices that have a strong evidence base, of which there are an increasing number. However, given the lack of methodologically sound studies of learning styles, it would be an error to conclude that all possible versions of learning styles have been tested and found wanting; many have simply not been tested at all. (p. 105)

As implied above, the determinations of experimental research calls for further deconstruction and review of cognitive and learning styles research in order to acquire a better understanding of its application in practice.

2.3.3.1 Theoretical specificity and confusion

The origins of individual differences as a notional construct was first

presented by Freeman in 1934 (Hered, 1935, p. 245). As Hered noted, “the assertion in Freeman’s book is that hereditary and environmental agencies are responsible for the development of mental traits”. After more than seven decades, this observation has remained relevant as the intelligence construct became more specific in the form of cognitive style, learning style, learning approach (or strategy) as some of the more commonly (and interchangeably) used concepts amongst many. One can surmise that such diversity and even overlap in definitions is necessarily an outcome of increased research in this area. However, specificity has also led to some confusion in its definition and usage which can be attributed to the perspectives from which these were measured by proponents of learning styles. The most common amongst these are those of Craik and Lockhart (1972) on Deep and Surface processing, Witkin and Goodenough (1981) on Field-Dependence/Field-Independence, and Riding and Cheema’s (1991) Wholist-Analytic and Verbaliser-Imager.

Information processing theory (McShane, 1991) has provided explanations about internal processes that take place as part of an individual’s reaction to the environment such as when textual information is presented to and interpreted by a learner. With its roots in communication and linguistic theories, information

processing theory attempts to explain the functional processes that operate to extract information when environmental stimuli are presented (McShane, 1991), particularly

in complex task performance (Snow, 1986; Witkin & Goodenough, 1981). Turing (1936) proposed that complex thought could be explained by a series of steps involved in processing specific and precise units of information. These strategies when used often enough, become spontaneous and are performed unconsciously, a predisposition referred to as learning styles. However, habitual and frequent mental processing are a precursor to, and predispose one to behave in spontaneous and consistent ways, a definitional element of cognitive styles. Indeed, Allport (1937) has defined cognitive styles as an “individual person’s typical or habitual mode of problem solving, thinking, perceiving and remembering”. As such, Entwistle (1981) is reasonable when he considers cognitive style and learning style as one and the same (Eyuboglu & Orhan, 2011).

Learning styles have been offered as the explanation for differences in performance that cannot be accounted for by ability. Cognitive and/or learning styles are used in real-world settings (school, work and the home). Cognitive and/or learning styles and eductive abilities are different yet interrelated, with the former described as an established schema for processing information and the latter measured as an aptitudinal trait. For example, Riding and Cheema’s (1991) classification of learners falls within two dimensions, Wholist-Analytic and

Verbaliser-Imager. Wholists like to process information by constructing an overall understanding or overview of the information and are able to work well with abstract concepts, learning in large steps. Analysts, on the other hand, like to process

information in detail and according to clear-cut conceptual groupings, and learning in small steps or increments. In the second dimension, Verbalisers like information to be presented in text form while imagers tend to represent information using diagrams and figures. In the context of higher education, Pillay (1998) found that

Wholist-Analytic and Verbaliser-Imager learners performed differently in computer- based tasks requiring recall, labelling and explanation. A more recent study has shown that reflective learners tended to be more analytic (Davies & Graff, 2006) and that the approach to testing cognitive or learning styles could inflate differences between learners.

2.3.3.2 Interdependency theories

Many of the theories and much of the research already mentioned attribute the formation of and influences on cognitive and/or learning styles to differences in information processing, personality, intelligence, ability and memory. The

fragmentation of cognitive/learning styles research based on personal characteristics such as gender, age and intelligence has been inconclusive in explaining how people react to learning procedures (Bock, 2001; Craik & Lockhart, 1972; Pashler,

McDaniel, Rohrer, & Bjork, 2008). Bock (2001) recognised the interdependencies of such variables, and attempted a synthesis of these often-paired and bipolar

variables using the Cognitive Orientation Index (COI). However, this novel attempt falls short of the need to look at how motivation, interest, experience, emotion, social and environmental effects may influence adaptive instruction (Snow, 1986; Tobias, 1989) where each element of such causality is investigated not necessarily

independent of each other but instead, interdependently. Biggs (1993, pp. 4-6) differentiates between cognitive styles and learning styles in that the former relates more to processes “adopted prior to, and which determine, the outcome of learning”. On the other hand, learning styles or what Biggs’ calls student approaches to

learning (SAL), are “predispositions to adopt particular processes”, so cognitive and learning styles are not wholly exclusive.

Horn and Cattell (1967; Sullivan, Johnson, Mercado, & Terry, 2009) posits that intelligence has hereditary origins (nature or arising from a person’s intrinsic potential) and that developmental intelligence can be attributed to various social and environmental factors such as better nutrition, more parental guidance, increased schooling and other influences that impact the growth and maturation process of the learner (nurture). The concept of nature or nurture as the source of intelligence or intellectual development were classified as fluid intelligence (natural ability) and crystallised intelligence (nurtured ability) by Horn and Cattell (1967; Sullivan, Johnson, Mercado, & Terry, 2009). The former refers to a person’s intrinsic ability to detect relationships and solve novel problems, while crystallised intelligence refers to learned and/or acquired knowledge of their culture for their own use. Fluid intelligence aligns with human development, peaking during the period of

adolescence and slowly declines during adulthood; in contrast, crystallised intelligence has the potential to increase throughout adulthood. The nature and nurture differentiation is supported by Gagné’s (2004) definition of giftedness and talent under the Differentiated Model of Giftedness and Talent (DMGT). Gagné designated giftedness as the “possession and use of outstanding natural abilities” (aptitudes - fluid) while talent, refers to the “outstanding mastery of systematically developed abilities” (competencies related knowledge and skills - crystallised).

Furthermore, Gardner’s theory of multiple intelligences (Gardner, 1999) and Sternberg’s triarchic theory of human intelligence (Sternberg & Grigorenko, 2001) present the learner as being equipped with a set of potentialities and strengths, abilities to develop and build on (Renzulli & Dai, 2001). These theoretical domains can be viewed with a holistic perspective through what Gardner describes as eight distinct forms of intelligence possessed by each individual in varying degrees:

verbal-linguistic, musical, logical-mathematical, visual-spatial, bodily-kinesthetic, intrapersonal (e.g., insight, metacognition), interpersonal (e.g., social skills) and natural intelligence (Gardner, 1999). Gardner (1989) defines intelligence as “the capacity to solve problems or to fashion products that are valued in one or more cultural settings”. Gardner’s cultural view of intelligence is complementary to the previously discussed view of cognitive ability (eductive ability) by Raven (1998) who highlights that the affective, conative and social interaction elements are inseparable to the internal components that influence the way information is

processed. Sternberg (1999), however, argues that intelligence should be taken from a combination of both rigorous empirical and socio-cognitivistic perspectives.

These theories purport that individual intelligence interacts with the external factors in the environment resulting in various developed and developing

competencies. However, in a contrary to view, Previde (1991) conducted the first cross-national validation of a translated version of the Kirton Adaption-Innovation lnventory which measures individual styles of problem definition and solving on a non-English speaking population sample and found that the derived Italian norms were closely similar to the English data, indicating that cognitive style was

uninfluenced by culture. Previde’s results were tested by Tullett (1997) through a

meta-analysis of English, French, Dutch, Italian, and Slovak studies. Tullett’s

findings, which were consistent with Previde, were supported by similarities between the psychometric properties and factor structures obtained for each of the language versions and A-I cognitive style varied more by occupation and work function than by nation. Lohman (1997), on the other hand, expanded the Gardner theory (1983; 1999) towards a more socio-historic view of intelligence as something that cannot be measured outside culture and experience. If one were to support such views, social

and environmental constructs influence how one interacts in an instructional environment, thereby shaping one’s stylistic approaches to learning tasks, whether cognitive/learning styles, preferences or approaches (Renzulli & Dai, 2001).

Therefore, intelligence has a relationship with and is viewed from a within social and environmental contexts. Biggs (1993) explained that SAL takes a socio-cognitivistic perspective where learning processes and approaches involve relationships between the student, teaching context, learning processes, and the learning outcome. So any measures to SAL should be interpreted in context. In a sense, the environmental agencies first proposed by Freeman in 1934 remains as relevant for further investigation today as it was 78 years ago.

2.3.3.3 The notional view of learning preferences, approaches and strategies

Up to this point, the discussion has presented much of the literature on cognitive processes and little of their externalisation in terms of what the learner does or strategies they use when they interact with the learning task. Early theorists including Pask (1976) who differentiated between students using a Serialist strategy (focus on details and step-wise approach) and Wholist strategy (focus on a broad view and building up the ideas in relation to the learning task), and Riding and Cheema (1991) who described the instructional preferences by Wholist-Analytic and Verbaliser-Imager learners as mentioned in Section 2.3.3.1. Theorists such as Pask (1976), and Marton and Saljo (1976) have used the terms style, strategy, process and instructional preferences and “terminologies abound in this area because none of the frequently used terms… have been rigorously defined, nor are there any universally agreed definitions” (Laurillard, 1979, p. 396). Laurillard offers the notion of learning processes to delineate executive style (refers to the way the student thinks

about the subject matter) and strategic approach (refers to the way the student approaches the task).

It is worthwhile at this point to look at these various schools of thought and theoretical perspectives (i.e., cognitive style and instructional learning preferences) through Curry’s (1983) onion model in order to see how these various schools of thought might be placed in relationship with each other. Curry’s model consists of four levels: 1) Cognitive personality elements consists of cognitive personality elements such as field-dependence and field-independence, and represents the most stable and fixed dimension. These elements are independent of the environment (Saddler-Smith & Riding, 1999) and approximates cognitive style; 2) Information processing style refers to the way learners process information but this can be affected by changing instructional methods, experience and motivation, and is akin to learning styles; 3) Instructional preferences refers to the learner’s choice of learning environment, is the least stable, is related to cognitive style, and is

influenced by the environment (learning preferences/approaches or strategies); and 4) Social interaction style (added later) refers to individual preference for social interaction while learning (socio-cognitivistic interplay and affect). The third and fourth dimension add an implicit view that internal processes are a function of the short- and long-term person-situation interaction, therefore, aptitudes are not a fixed entity. The notion of context and externalisation of learning relative to the local environment cannot be discounted.

Renzulli and Dai (2001, p. 33) proposed a person-situation interaction perspective that sees “abilities as outcomes as well as antecedents of human

interaction with task demands and opportunities, interests as emergent self-direction and self-differentiation, and cognitive learning styles as emergent modes of

information processing and self-expression in the person-situation interaction”. Earlier, Snow (1986, p. 11) had espoused that, “Fully articulated, studied, and applied, such a view would represent a true paradigm shift in educational theory, research, and practice”. Learning approaches and abilities (critical thinking and

eductive ability) as relational constructs with self-efficacy (technological) and the socio-environmental influences that play a role in individual development merits

discussion. Therefore, in this study, these relational constructs will be explored in the performance of complex and contextualised learning tasks involving learning and critical thinking, whilst engaging with a mediating and/or anchoring technology. However, before complex tasks and contextualisation are explained, motivation and technological self-efficacy are first discussed below.