J. of Acc. Ed. 22 (2004) 29–52 www.elsevier.com/locate/jaccedu The role of cognitive learning styles in accounting education: developing learning competencies Angus Duff* Accounting, Finance and Law, Paisley Business School, University of Paisley, University Campus, Ayr, Beech Grove, Ayr KA8 OSR, UK Received 1 July 2002; received in revised form 1 August 2003; accepted 1 September 2003 Abstract The potential for cognitive learning style (CLS) to develop students’ learning competencies is limited by the variety of conceptualizations, constructs and instruments. This paper contrasts two models for operationalizing CLS: Furnham’s [Furnham, A. (1995). The relationship between personality and intelligence to cognitive style and achievement. In D. H. Saklofske, M. Zeidner (Eds.), International handbook of personality and intelligence (pp. 397– 413). New York: Plenum Press.] conceptualization of the roles of CLS, and Ramsden’s [Ramsden, P. (1992). Learning to teach in higher education. London: Routledge.] contextual model of student learning. The origins of CLS, its fundamental dimensions, and methods of assessment are also reviewed. Five propositions suggesting ways accounting educators can make use of CLS and associated measures to help students ‘learn how to learn’ are developed and recommendations for future research are offered. # 2003 Elsevier Ltd. All rights reserved. Keywords: Cognitive learning styles; Cognitive information processing models; Students’ approaches to learning; Learning how to learn 1. Introduction Calls to develop accounting students’ personal and interpersonal skills, in addition to teaching technical material, have been consistently made over the past two decades (AAA, 1986; AECC, 1990; AICPA, 1997, 1999; Albrecht & Sack, 2001; Williams, 1999). Central to the developing personal competencies is understanding * Corresponding author. Tel.: +44-1292-886296; fax: +44-1292-886250. E-mail address: angus.duff@paisley.ac.uk (A. Duff). 0748-5751/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaccedu.2003.09.004 30 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 accounting students’ learning styles and preferences. Identifying a suitable framework for understanding how learning takes place in an accounting context is, however, not straightforward. The notion of (cognitive) learning style has been with us for more than 40 years.1 The pioneering work of Witkin and Asch (1948) reported experimental evidence of individual differences in information processing strategies. These consistent cognitive differences have been referred to as ‘cognitive learning style’ (CLS) (Witkin, Moore, Goodenough, & Cox, 1977). Accounting educators have been keen adopters of CLS research. Three principal areas of interest to those applying CLS within accounting education are to improve educational effectiveness, to identify differences within student groups, and to predict career choice or choice of major. The purposes of this paper are to: 1. present an overview of CLS; 2. contrast two theoretical models where CLS is an intervening variable; 3. provide a framework to understand the relationship between the contrasting theories and approaches; 4. identify and describe the associated measurement instruments, and provide some information on their psychometric properties; 5. describe the application of CLS by accounting education researchers; 6. identify the utility of the theories and instruments to accounting education researchers and suggest some future avenues for research. This paper is divided into three parts. The first part describes two conceptual models that identify the antecedents of CLS. The second part of the paper classifies CLS models into two categories: (i) cognitive information processing models and (ii) students’ approaches to learning. Within this section we summarize the work undertaken by accounting educators using CLS and identify some potentially fruitful avenues for accounting education research. Finally, we discuss some implications of the adoption of CLS by accounting educators. 2. Theoretical sources of CLS models Two fundamentally different models underlie CLS research. The first model, described by Furnham (1995), considers CLS to be a central and powerful moderator variable’’ (p. 398) in the relationship between personality, learning, memory, and academic achievement—see Fig. 1. Furnham’s analysis suggests three things. First, personality and intelligence are independent predictors of academic achievement. Second, personality and intelligence can predict CLS, which in turn is a moderate of academic performance. Third, teaching and assessment methods are independently related to CLS in that teaching and assessment methods may—or 1 Although the term ‘‘cognitive style’’ was coined by Allport (1937) as referring to an individual’s typical way of solving problems, thinking, perceiving and remembering. A. Duff / J. of Acc. Ed. 22 (2004) 29–52 31 may not—‘match’ an individual’s preferred CLS.2 Importantly, these researchers tend to draw from cognitive psychology in exploring how learning takes place as students process information. A second model (Ramsden, 1992), described as a model of student learning in context, focuses on the three stages of presage, process and product. Known as the 3Ps model, this model is presented in Fig. 2. Ramsden’s model articulates the Fig. 1. Relationship between intelligence, personality, cognitive learning style and achievement (adapted from Furnham, 1995). Fig. 2. Presage–process–product (3Ps) contextual model of student learning (Ramsden, 1992). 2 See Hayes and Allinson (1993) for a discussion of the efficacy of the matching hypothesis. 32 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 thinking of educational psychologists from the so-called Student Approaches to Learning (SAL) school, the development of which is described in greater detail later within this paper. SAL researchers see learning as contextually-based and ‘bottomup’ and criticize traditional theoretical sources, as outlined by Furnham (1995) as being ‘top-down’ and ‘acontextual’. Although traditional CLS models, as exemplified by Furnham (1995), focus on individuals, SAL researchers claim their instruments are based on a theoretical rationale grounded in how students actually go about learning tasks in educational settings (e.g. classrooms and lecture halls) (Watkins, 1998). The 3Ps model suggests that the quality of learning (in terms of learning outcomes sought or desired) is influenced by a student’s approach to learning. A student’s approach to learning is influenced by perceptions of the task requirements, which in turn are influenced by prior educational experiences and the context for learning (e.g. the curriculum, teaching processes, and assessment). Consequently, SAL researchers tend to play down the role that intelligence (or cognitive ability) and personality might play in determining learning outcomes, focusing more on prior educational experience and the context of learning—which can be directly influenced by educators. 3. Classifying CLS models As Furnham’s (1995) conceptualization identifies, CLS measures lie somewhere between aptitude/ability measures and personality measures. CLS has been viewed as a structure or content, as a process, or as both structure and process (Riding & Cheema, 1991). When the focus is on structure, then CLS is seen as a relatively permanent and enduring construct. Consequently, in an educational setting, CLS is seen as a trait of the individual student. When CLS is viewed as a process, the focus moves to how students’ CLS may be changed. Researchers and practitioners who utilize process CLS models, see style as dynamic and malleable, creating educational interventions which can be used to compensate for weaknesses and build on existing strengths. Others view CLS as both a process and structure, being relatively stable yet capable of change. The next part of this paper describes specific learning style models and associated measures, provides some evidence of their psychometric properties, explains how the models have been used by accounting education researchers, and discusses the potential for learning style models for guiding future accounting education research. 4. Cognitive information processing models Information processing style can be thought of as an individual’s approach to assimilating information. Sadler-Smith (2002) argues that the heightened interest in cognitive information processing in management-related disciplines can be attributed to a recognition that the ability to assimilate, process, and respond to increasing amounts of information is a key competency for today’s managers. Examples of cognitive information processing (CIP) models include: Kolb’s Experiential Learn- A. Duff / J. of Acc. Ed. 22 (2004) 29–52 33 ing Model (ELM) (1976), Cognitive Styles Analysis (Riding, 1991); Cognitive Styles Index (Allinson & Hayes, 1996); and the Kirton Adaptation Index (Kirton, 1976, 1994). CIP models are summarized in Table 1. Riding (2001) and Sadler-Smith (2002) argue that two super-ordinate categories of cognitive information processing models exist. The first addresses the mode of representing information, while the second model addresses the mode of organizing and processing information. 4.1. Kolb’s ELM Kolb’s ELM describes learning in terms of processes rather than outcomes. In this sense, Kolb’s ELM can be thought of as a cognitive information processing model conceived as a mode of organizing and processing information. The ELM has four distinct stages: (i) concrete experience, (ii) observation and reflection, (iii) formation and generalization of abstract concepts, and (iv) the testing of these concepts in new situations, leading to further concrete experiences. Each stage can be conceived as a level of ability, in that an ideal learner has the capacity to operate with equal facility at all four stages. However, most learners will have a preference for one or more stages in the process (or cycle). Three self-report inventories exist which purport to identify an individual’s preferred CLS: the Learning Style Inventory (LSI) (Kolb, 1976); a later revision of the LSI (LSI-1985) (Kolb, 1985); and the Learning Styles Questionnaire (LSQ) (Honey & Mumford, 1986, 1992). Both the LSI-1976 and LSI-1985 identify four learning styles: reflective observation (RO), abstract conceptualization (AC), active experimentation (AE), and concrete experience (CE). The LSQ measures four dimensions, which correspond approximately to those suggested by Kolb: Activist (c.f. AE), Reflector (c.f. RO), Pragmatist (c.f. CE), and Theorist (c.f. AC). ELM instruments have received substantial criticism for their poor measurement qualities (see Ruble & Stout, 1994 for a review of the LSI and LSI-1985 and De Ciantis & Kirton, 1996; Duff & Duffy, 2002; Swailes & Senior, 2000 for empirical evidence considering LSQ scores). The LSI has recently undergone revision to create a third version (Kolb, 1999a, 1999b), and two further ELM instruments: the Adaptive Style Inventory (AdSI) (Boyatzis & Kolb, 1993), and the Learning Skills Profile (LSP) (Boyatzis & Kolb, 1991), described in Mainemelis, Boyatzis, and Kolb (2002). Empirical results from Mainemelis et al., (2002) indicate that LSI-1985, ASI, and LSP ‘‘show a high degree of commensurability’’ (p. 22). Research correlating the LSQ with personality measures indicates that the Activist–Reflector bipolar scale is closely related to extraversion (Furnham, 1996; Jackson & Lawty-Jones, 1996), which suggests that learning style is a subset of personality. 4.1.1. Cognitive Styles Analysis (CSA) (Riding, 1991) The development of the CSA reflects a synthesis of previous work in CLS in that the CSA attempts to integrate elements of style theory in one CLS model (Riding & Cheema, 1991; Riding & Rayner, 1995). Riding’s (1991) model consists of ‘two basic dimensions of style’: the Wholist–Analytic (W–A) style dimension of whether an 34 Table 1 Descriptions and dimensions of cognitive learning style- cognitive information processing models category Description References Cognitive Styles Analysis (CSA) Wholist–Analytical Verbalizer–Imager The tendency to process information: in parts or as a whole The tendency to for the individual to think in: words, or pictures Riding (1991) Cognitive Styles Index (CSI) Reasoning–Intuitive Active–Contemplative A preference for developing understanding by: using reasoning; or spontaneity/insight A preference for learning activity which allows for active participation, or passive reflection Kirton Adaptation Index (KAI) Adaptor–Innovator Learning Styles Inventory (LSI; LSI-1985) Concrete experience; Reflective observation; Abstract conceptualization; Active experimentation. Learning Styles Questionnaire (LSQ) Activist Theorist Reflector Pragmatist Allinson and Hayes (1996) Kirton (1976, 1994) A bi-polar scale. Adaptors have a preference for ‘doing things better’, at the other end of the continuum, Innovators prefer to do things differently A model consisting of two bipolar scales emphasizing: abstractness over concreteness (labelled prehension), and action over reflection, (labelled transformation). Learning style is defined in terms of four learning styles based on Kolb’s (1976) Experiential Learning Model. Each learning style is a preferred mode of learning, which is said to describe an individual’s approach to learning. Kolb (1976, 1985) Honey and Mumford (1986, 1992) A. Duff / J. of Acc. Ed. 22 (2004) 29–52 Model A. Duff / J. of Acc. Ed. 22 (2004) 29–52 35 individual tends to process information as a whole or in component parts and the Verbal–Imagery (V–I) style dimension describing whether an individual is inclined to represent information verbally or in mental pictures. The CSA measures both super-ordinate categories of cognitive information processing: the W–A dimension is a means of organizing and processing information, the V–I dimension a means of representing information. It is suggested that the W– A style dimension ‘‘probably assesses a dimension related to’’ the Intuition–Analysis dimension of the CSI (Riding, 1997). Riding (2001) provides electro-encephalogram (EEG) evidence to support a physiological basis to both W–A and V–I styles. Although evidence exists for the validity of scores produced by the CSA (e.g. Riding & Agrell, 1997; Riding & Craig, 1999), further investigation indicates that the measure has poor internal consistency reliability and test re-test reliability (Peterson, Deary, & Austin, 2003a). Peterson et al. (2003a) indicate the psychometric properties of scores can be improved further by doubling the number of items on both scales. Riding (2003), replying to Peterson et al.’s (2003a) critique, argues that expanding the test increases the chance of respondent fatigue. Replying to Riding’s (2003) commentary on their work, Peterson, Deary, and Austin (2003b) conclude that Riding’s comments ‘‘merely distract from rather than criticise, our simple, novel, positive finding that the reliability of the W-A dimension of the CSA can be improved’’. 4.1.2. Cognitive Styles Index (CSI) (Allinson & Hayes, 1996) The CSI was developed as a measure of CLS for use in a professional context (specifically a business management context). The measure focuses on a single dimension of style: Intuition–Analysis, a style dimension conceived as a result of Allinson and Hayes’ (1988, 1990) earlier work considering the learning styles of managers using the LSQ. Their exploratory factor analytic studies consistently produced two factors that they labelled ‘Analysis’ and ‘Action’. The development of the CSI is an extension of this earlier work, where the ‘Action’ construct is relabeled ‘Intuition’. Intuition is thought of as an immediate reaction based on feelings, and analysis is a judgment based on rationality. To avoid confusing analytical and rational processing, the CSI ‘Analysis’ pole will be described as a ‘Rational’ style (Sadler-Smith, 2002). Allinson and Hayes (1996) claim the Intuition–Rational dimension reflects the duality of human consciousness, in the tradition of Robey and Taggart (1981), and their style of problem-solving which can be described as either intuitive or rational. It is claimed that the Intuitive– Rational dichotomy represents a long established difference in contrasting modes of thought (Nickerson, Perkins, & Smith, 1985). The Intuition–Rational dimension can be thought of as a means of organizing and processing information. Intuition and Rational styles are said to characterize right-brain and left-brain thinking, as Allinson and Hayes (1996 p. 122) suggest: Intuition, characteristic of the right brain orientation, refers to immediate judgment based on feeling and the adoption of a global perspective. Analysis, characteristic of the left brain orientation, refers to judgment based on mental reasoning and a focus on detail. These right-left patterns are not merely tran- 36 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 sient; people seem to have a rather permanent stylistic orientation to the use of one hemisphere. Estimates of internal consistency reliability for CSI scores vary from 0.78 to 0.90 (Allinson & Hayes, 1996; Armstrong, Allinson, & Hayes, 1997; Murphy, Kelleher, Doucette, & Young, 1998; Sadler-Smith, Allinson, & Hayes, 2000). Factor analytic techniques and correlation with other measures of individual difference provide some evidence of construct validity (Allinson & Hayes, 1996). 4.1.3. Kirton Adaptation–Innovation Index (KAI) (Kirton, 1976, 1994) Kirton’s model of cognitive style assumes that style is related to an individual’s preferred cognitive strategy in response to change. Such strategies are associated with creativity, problem-solving and decision-making. In turn, these strategies are said to relate to simple aspects of personality (traits) that manifest themselves early in life. Kirton’s bipolar dimension Adaptation–Innovation is said to be stable over both time and incident. Adaptors tend to like to ‘do things better’, while an Innovator has a preference for ‘doing things differently’. Similarly, when solving problems, Adaptors prefer structured tasks, while Innovators prefer unstructured situations. Psychometric testing of the KAI indicates that the measure has high internal consistency reliability (a coefficients ranging from 0.76 to 0.91), high test–retest reliability over time periods from 7 to 41 months (r from 0.82 to 0.86), and high construct validity in a range of cultural contexts (see Kirton, 1994, pp. 14–19). 4.2. Empirical evidence of relationship between different cognitive information processing instruments Sadler-Smith (2001) examined the relationship between Kolb’s learning styles (using the LSI) and cognitive style (using the CSA), reporting non-statistically significant correlation coefficients of slight magnitude (r ranging from 0.07 to 0.11) between the four dimensions of the LSI and the two bipolar dimensions of the CSA. These results lend support to previous theoretical categorizations that the dimensions of Kolb’s ELM and Riding’s CSA are independent. No empirical research has considered the relationship between the KAI and either the CSA or CSI. Conceptually, the Adaptor–Innovator dimension of the KAI bears similarities to that of the Rational–Intuition dimension of the CSI, with empirical evidence positively relating the KAI to a measure of left–right brain style of thinking (Torrance, Reynolds, Ball, & Riegel, 1978) which underlies the bipolar dimension of the CSI. Fig. 3 provides a conceptual illustration of CIP models. 4.3. Prior application of CIP models in accounting education Accounting educators were quick to recognize the potential of Kolb’s (1976) ELM. The first paper (Baldwin & Reckers, 1984) to examine students’ preferred CLS in an accounting education context used the LSI-1976 and considerable research using this measure has been conducted by a number of authors, mainly in A. Duff / J. of Acc. Ed. 22 (2004) 29–52 37 Fig. 3. Mindmap (Buzan, 1995) of cognitive information processing models. the United States. Some research has questioned the validity of the LSI: using a test– retest design (Stout & Ruble, 1991a), by undertaking a psychometric evaluation (Stout & Ruble, 1991b), and by identifying similar problems with the LSI-1985 (Ruble & Stout, 1993; Stout & Ruble, 1994).3,4 Few accounting education researchers have utilized the LSQ. Duff (1997a) in a correlational study sampling UK accounting students (N=142) reported that the LSQ showed unsatisfactory internal consistency reliability, construct validity, and predictive validity. Sangster (1996) in a study of UK accounting students,5 related scores on the LSQ to their preference for assessment using computer-based objective testing. Duff (1998) drawing on his experience with using the LSQ and the results of validity studies in other disciplines, indicated that Sangster’s (1996) application of the LSQ was premature. Recent work in accounting has also indicated that the LSQ produces scores with unsatisfactory measurement properties and is unsuitable for use in applied research or observational studies (Duff, 2001a). 3 Despite these criticisms, both versions of the LSI and LSQ are commonly used as a pedagogic tool— see Loo (1997) for an informed discussion. 4 Our review of the application of the LSI-1976, LSI-1985 is deliberately brief given the measurement problems of the instrument described in the collective work of Ruble and Stout. 5 The sample size of this study is undisclosed. 38 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 Gul (1986) was the first to apply Kirton’s adaptation–innovation theory to accounting education. Using a sample of 26 Australian undergraduate students, Gul identified a majority as being classified in the adaptor, rather than innovator, category. Wolk and Cates (1994) used Kirton’s (1976) KAI to investigate differences between problem-solving styles of samples of accounting students (N=39) and other business students (N=118) in the US. Differences between the two samples were not found, with accounting students more likely to be adaptors than other business students.6 In a study of US accounting faculty (N=82), Wolk, Schmidt, and Sweeney (1997) report a greater percentage of faculty as using an adaptor style versus an innovative style. The authors note that KAI scores were correlated with certain pedagogical perceptions and preferences, as measured by a survey instrument. Arunachalam, Sweeney, and Kurtenbach, (1997), using samples of accounting students (N=145) in the US, investigated the relationship between scores produced on Kirton’s (1976) KAI and student performance on both a structured and an unstructured task. Similar to the findings of Wolk and Cates (1994), the majority (72%) of students were classified as adaptors, with the minority (28%) as Innovators. As hypothesized, students classified as innovators outperformed those classified as Adaptors on the unstructured task. However, no statistically significant difference was reported between the performance of Adaptors and Innovators on the structured task. A review of the literature found no applications of Riding’s (1991) CSA or Allinson and Hayes’ (1996) CSI to samples of accounting students or practitioners. 4.4. Accommodating CIP theories: implications for accounting educators Kolb’s ELM has received particular attention in accounting education. Research has shown that associated measures (LSI-1976, LSI-1985, LSQ) yield scores of doubtful validity. Consequently, the ELM is unsuitable for applied research until a measure producing scores of satisfactory psychometric properties is created. It is surprising that accounting educators have not adopted either the CSA or CSI. Riding’s concept of cognitive style suggests two propositions. The first is that: Proposition 1. Accounting education that accommodates individual differences in Verbal and Imager styles by the adoption of appropriate instructional and learning strategies will lead to enhance learning performance and increase the ability of the student to ‘learn how to learn’. For example, instructors accommodate both V–I styles by verbally presenting information to accommodate a Verbal style and visually presenting information to accommodate the Imagery style. The use of techniques such as mind mapping (Buzan, 1995), illustrated in Fig. 3 could be used to accommodate an Imagery style. Conversely, word association games, where students link unexpected words with immediate 6 Although calls have been made to encourage accounting educators to assist students to develop innovative problem-solving skills, Wolk and Cates (1994) report evidence that accounting students may be fit this category. Kirton’s adaptation–innovation theory suggests these construct are stable. Consequently intervention strategies may be futile. A. Duff / J. of Acc. Ed. 22 (2004) 29–52 39 responses, making connections without logical thought (Parker & Stone, 2003) could be used to accommodate Verbalizers. Furthermore, if collaborative learning is being used, an awareness of individual differences in representing information (e.g. participants represent information diagrammatically as well as in verbal form) should enhance collective learning. Unlike the Verbal and Imager styles of representing information, both the Wholist and Analytic styles of processing information have complementary limitations. A Wholist may see the so-called ‘big picture’ but struggle to identify structures within the overall concept. Likewise, an individual with an Analytic style may understand particular elements, but have difficulty in seeing how the elements fit together. To support a Wholist style, an instructor might adopt a highly structured route through learning, explaining the relationships among concepts, which their global style of processing information may miss. Providing a content overview to illustrate how a particular concept, which might otherwise be overemphasized, fits into the overall structure could accommodate an Analytic style. These ideas are captured in proposition 2, which is specified as: Proposition 2. Accounting education that accommodates individual differences in Wholist and Analytic styles by the appropriate instructional and learning strategies will enhance learning and increase the ability of the student to ‘learn how to learn’. Similar to the Wholist–Analytic bipolar styles, Adaptor/Analysis and Innovator/ Intuition styles have specific advantages and limitations in particular learning situations. Therefore, it is desirable to encourage the individual to develop new strategies to complement their primary style. Geary and Rooney (1993), reviewing the contribution accounting teaching materials make to the learning process, note that accounting education has historically been based on sensate thinking, rather than intuitive thinking. Consequently, if accounting educators are to improve their students’ learning competencies, materials that help students develop intuitive thinking skills should be used. Intuitive skills could be developed by exposing students to more unstructured problems, encouraging the querying of a problem’s concomitant assumptions, and by presenting a range of paradigms which might cut across a particular problem or situation. These ideas lead to proposition 3 Proposition 3. Accounting education that accommodates individual differences in Adaptor (Rational) and Innovator (Intuition) styles by the appropriate instructional and learning strategies will lead to enhance learning performance and increase the ability of the student to ‘learn how to learn’. 5. Students’ Approaches to Learning (SAL) Marton and Saljo’s (1976) seminal work found that students exhibited two contrasting approaches to reading academic articles and texts: a ‘deep’ approach and a 40 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 ‘surface’ approach. A deep approach entails looking for meaning in the matter being studied and relating it to other experiences and ideas. A surface approach can be thought of as a reliance on rote learning and memorization in isolation from other ideas. It is generally held that the development of a deep approach is consistent with the avowed aims of higher education (Hayes, King, & Richardson, 1997). A deep approach to learning is likely to result from relevance to students’ interests (Fransson, 1977), the interest, support and enthusiasm shown by the instructor (Ramsden, 1979), and letting students manage their own learning (Ramsden & Entwistle, 1981). Researchers employing questionnaires to assess students’ approaches to studying have extended Marton and Saljo’s work. Measuring students’ approaches to learning has been seen as a means of: (i) encouraging a more systematic approach to academic teaching (Katz & Henry, 1988); (ii) assisting individual academics who want to monitor and improve the effectiveness of their own teaching (Richardson, 1990); (iii) identifying students who are ‘at risk’ because of ineffective study strategies (Tait & Entwistle, 1996); (iv) observing the outcomes (Biggs & Collis, 1982) and experience of learning (Marton, Hounsell, & Entwistle, 1984); and (v) evaluating the quality of student learning (Meyer & Muller, 1990). The four most widely applied instruments for evaluating students’ approaches to learning are: (1) Entwistle, Hanley, and Hounsell’s (1979) development of the Approaches to Studying Inventory (ASI) in the UK, (2) Biggs’ (1978, 1987) Study Processes Questionnaire (SPQ) developed in Australia, (3) Vermunt’s (1992) Inventory of Learning Styles (ILS), which is popular in continental Europe, and (4) Schmeck, Ribich and Ramaniah’s (1977) Inventory of Learning Processes (ILP) developed in the United States. The properties of these four measures are summarized in Table 2. The development of each of these measures is described in the next section. 5.1. Approaches to Studying Inventory (ASI) (Entwistle et al., 1979) Entwistle and his colleagues’ contribution to our understanding of CLS is a continuation of Marton and Saljo’s (1976) concepts of ‘deep’ and ‘surface’ approaches. Entwistle et al (1979) linked students’ preference for instructional methods to their level of information processing when learning; that is, a deep or surface approach to the task. In its most commonly used version, the ASI contains 64 items in 16 scales (Entwistle & Ramsden, 1983). A later version of the ASI includes the RASI (Entwistle & Tait, 1995), which has 44 items in six scales: deep approach, surface approach, strategic approach, lack of direction, metacognitive awareness of studying, academic self-confidence. 5.1.1. Study Processes Questionnaire (SPQ) (Biggs, 1987) Biggs’ (1978) Study Processes Questionnaire (SPQ) is similar to Entwistle et al.’s (1979) ASI. The major difference is that the Biggs model includes motivational factors for the previously identified deep and surface processing activities. The factors relate to intrinsic, extrinsic, and achievement motivation. However, empirical work has shown the SPQ to have unsatisfactory measurement properties (Christensen, Massey, & Issacs, 1991; Kember & Gow, 1991; Kember, Wong, & Leung, 1999; Table 2 Descriptions and dimensions of cognitive learning style- students’ approaches to learning category Model Approaches to Studying Inventory (ASI) Meaning orientation; References An individual’s preferred approach to studying, which makes explicit reference to their instructional preference Entwistle et al. (1979) An individual’s preferred orientation to learning consisting of four dimensions: Vermunt (1992) A framework identifying the ‘quality of thinking’ which occurs during learning relating to the distinctiveness, transferability and durability of memory Schmeck et al (1977) An individual’s preference for approach to studying, including a study motive element Biggs (1978, 1985) Reproducing orientation; Achieving orientation; Holistic orientation. Inventory of Learning Styles (ILS) Undirected Reproduction directed Application directed Meaning directed Inventory of Learning Processes (ILP) Synthesis-analysis; Elaborative processing; Fact retention; Study methods. Study Processes Questionnaire (SPQ) Surface–deep (achievement orientation); Intrinsic–extrinsic (achievement orientation). A. Duff / J. of Acc. Ed. 22 (2004) 29–52 Description 41 42 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 O’Neil & Child, 1984). The instrument has recently undergone revision, reflecting its development over 20 years ago to create the R-SPQ-2F (Biggs, Kember, & Leung, 2001). The R-SPQ-2F is reported as possessing satisfactory measurement properties, at least when administered to samples of health science students in Hong Kong (N=229, and 495) (Biggs et al., 2001). 5.1.2. Inventory of Learning Styles (ILS) (Vermunt, 1992) Vermunt’s (1992) ILS is widely applied in continental Europe for the assessment of approach to learning. The English language version of the instrument consists of 100 items in two sections addressing ‘study activities’ and ‘study motives’. Vermunt distinguishes four learning styles: undirected, reproduction directed, application directed, and meaning directed. The undirected and reproduction directed styles are approximately equivalent to Entwistle and his co-workers notion of a surface approach. Application directed and meaning directed styles are similar to the concept of a deep approach. The four distinct learning styles have been confirmed using samples of students in traditional university environments (Boyle, Duffy, & Dunleavy, 2003; Busato, Prins, Elshout, & Hamacker, 1998; Vermetten, Lodewijks, & Vermunt, 1999) and in distance learning environments (Vermunt, 1998). 5.1.3. Inventory of Learning Processes (ILP) (Schmeck et al., 1977) Using Craik and Lockhart’s (1972) notion of ‘levels of processing’, Schmeck et al. (1977) elaborated a theory of learning which relies on a concept of ‘quality of thinking’. The quality of thinking, is said to affect the distinctiveness, transferability, and durability of memories that result from the learning event (Schmeck, 1988). Schmeck et al. (1977) operationalized this theory by the development of the Inventory of Learning Processes (ILP), which purports to measure CLS using four scales: synthesis-analysis, elaborative processing, fact retention, and study methods. 5.2. Prior application of SAL research within accounting education A search of the literature revealed eight published empirical studies of accounting students’ approaches to learning and two literature reviews (Beattie, Collins and McInnes, 1997; Lucas, 1996). The empirical investigations utilized three instruments: Biggs’ (1987) SPQ was used by Booth, Luckett, and Mladenovic (1999); Davidson (2002); Eley (1992); and Gow, Kember, and Cooper (1994); Schmeck et al.’s (1977) ILP was used by Duff (1997a) and Tan and Choo (1990); and Entwistle and Tait’s (1995) RASI was used by Duff (1999) and Hassall and Joyce (2001). Tan and Choo (1990) were the first to apply SAL to accounting education. Administering the ILP to a sample of 89 undergraduate accounting students in Australia, the authors report students obtaining high scores on the deep processing and elaborative processing scales significantly outperforming those achieving low scores on these two subscales. However, Duff (1997a) reported that the ILP had A. Duff / J. of Acc. Ed. 22 (2004) 29–52 43 poor psychometric properties when applied to samples of UK accounting students (N=142).7 Eley (1992), in a study of a mixed sample of Australian undergraduate accounting (N=63) students, using the SPQ, reported the accounting students exhibited higher scores for surface approach and lower scores for deep approach, than science and English literature students. Gow et al., (1994) investigated SAL in Hong Kong (N=793) using the SPQ as part of a longitudinal study. They report that as accounting (and other) students progressed through their programme of study, they became more inclined to adopt a surface approach and less inclined to adopt a deep approach. The authors suggest these findings may be attributable to a number of factors including: excessive workload, assessment methods, didactic teaching style, a high staff to student ratio, and a possible effect of being taught in a second language. Duff (1999) administered a 30-item short-form of the RASI (Duff, 1997b) to two independent samples of UK accounting and business students (N=179, 137) to explore the relationship between students’ approaches to learning and their educational background, age, and gender. No main effects were reported in the relationship between approaches to learning and students’ educational background. However, mature students (25 years or older) scored higher on deep approach and strategic approach than their younger counterparts and self-reports indicated that females were more likely than males to adopt a surface approach. Administering a 38-item RASI to four cross-sectional samples of professional accounting (CIMA) students (N=547), Hassall and Joyce (2001) reported that surface approach scores declined over the four stages of the CIMA qualification, whilst deep approach scores remained stable. Booth et al. (1999) compared scores on the SPQ for samples of Australian accounting undergraduate students (N=374) to previously reported norms for Australian arts, education, and science students. The accounting students were found to have relatively higher surface approach and lower deep approach scale scores than did the other student groups. Furthermore, higher surface approach scores were found to be associated with less successful academic performance. Davidson (2002) examined the relationship between Canadian accounting students’ approaches to learning, measured by the SPQ, and their examination performance. Although deep approach scores were positively related to performance in complex examination questions, no relationship was reported between deep approach and performance on less complex questions or mean grades. Surface approach scores were not related to any aspect of academic performance. Although Davidson (2002) did not report the measurement qualities of SPQ scores, the relatively low predictive power of the investigation may reflect the psychometric limitations of the scores the SPQ yields. 7 The seemingly contradictory findings of Duff (1997a) and Tan and Choo (1990) may be explained by: no psychometric evidence being reported by Tan and Choo; and cultural differences between samples. 44 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 5.3. Students’ approaches to learning: implications for accounting educators To summarize, the recent application of the approaches to learning paradigm in accounting education has been vigorous and has shown some utility to inform accounting educators of how their students learn. The next part of this paper develops two propositions to identify how accounting educators could successfully use SAL measures and the 3Ps model in their own teaching. These propositions focus on improving teaching practice and identifying students ‘at risk’ of failure due to ineffective study strategies. 5.4. Improving teaching practice using the 3Ps model The 3Ps model (Ramsden, 1992) shown in Fig. 2, emphasizes that the quality of learning outcome is directly related to a student’s approach to learning. The development of desirable (i.e. deep and strategic) approaches is dependent both on an awareness of a student’s orientation to learning as well as the contextual dependency of teaching and learning. This leads to proposition 4 Proposition 4. Accounting educators should recognize that the quality of learning is directly influenced by students’ approaches to learning, which in turn depend on both an awareness of the contextual dependency of learning and teaching. Research applying the ASI measure indicates that approaches to learning and associated learning outcomes may differ among disciplines (Entwistle, 1984; Meyer, Parsons, & Dunne, 1990; Meyer & Watson, 1991). In general, liberal arts students are believed to display higher levels of intrinsic interest in their studies and adopt a deep approach, whilst students in vocational disciplines are more motivated by vocational concerns and adopt a surface approach (Ramsden & Entwistle, 1981; Watkins & Hattie, 1981). In this sense, the perceptions and experiences of the teaching and learning context may be shaped by the epistemology of the discipline (Lucas, 2001; Meyer & Eley, 1999). Although some contextual variables, for example, students’ need to work part time, are outside the control of accounting educators, variables such as instructional and assessment methods and workload are determined by educators and administrators. Assessment is one of the most important contextual variables that influences approach to learning (Tang, 1998). Accounting educators should look to adopt methods that assess cohesive and structural qualities of learning, rather than discrete quantities of knowledge. For example, multiple-choice test questions and essay questions that are marked to preset answers, with marks awarded for each piece of correct knowledge, encourage rote-learning and memorization strategies; that is, a surface approach to learning. Continually-assessed projects, learning portfolios, and essay questions that encourage students to demonstrate the quality and integrity of their learning promotes active learning, which helps facilitate a deep approach. Cooperative learning has been widely applied in the field of accounting education, and has been shown to encourage a deeper approach and improve the quality of learning outcomes (see Tang, 1998 for a recent review). A. Duff / J. of Acc. Ed. 22 (2004) 29–52 45 5.5. Identify students ‘at risk’ due to poor learning strategies Research applying the ASI generally finds that academic performance is positively correlated with strategic approach and negatively correlated with surface and apathetic approaches (Entwistle & Ramsden, 1983). High scores on deep approach are positively related to academic performance when the assessment procedure directly favours the demonstration of conceptual understanding (Entwistle, Tait, & McCune, 2000). Consequently, deep approach and strategic approach are conceptually related as components of effective studying, with surface approach negatively related to strategic approach. This conception is analogous to Janssen’s (1996) categorization of an effective student—a studax—characterized as employing an approach of depth and strategy. Conversely, students scoring high on surface approach and low on strategic approach are considered ineffective learners (Entwistle, McCune, & Walker, 2001). This leads to a final research proposition: Proposition 5. Accounting educators have the capacity to identify students with ineffective study strategies—using SAL measures—which are likely to impair their academic performance and increase the probability they will withdraw from the accounting program. Cluster analysis has explored the patterns of response using the ASI and later variants to identify sub-groups which vary in terms of their levels of attainment and background (Entwistle et al., 2000; Meyer, 1991; Meyer and Muller, 1990; Meyer et al., 1990). These studies have typically uncovered one persistent low attainment cluster, displaying what has been described as a ‘‘dissonant pattern of response’’ (Entwistle et al., 2000 p. 33). Analysing these investigations suggests the following: First, administering the SAL measures to students and providing them with feedback about the results may encourage students to be more self-aware, develop an understanding of the determinants for success in accounting programs, and encourage them to seek assistance when they encounter difficulty with their studies. Second, scores on SAL inventories will provide information that instructors can use to identify ‘at risk’ students. Support and counseling, either on an individual or group basis, can then be provided to these students. Duff (2002) reports that a cluster analysis of 60 UK accounting and business economics first-year (freshman) students’ scores on the RASI reveals two clusters, labelled ‘effective learner’ and ‘ineffective learner’. The ‘effective learner’ has a 75% rate of progression, defined as scoring a pass in all subjects studied, whilst the ‘ineffective learner’ cluster is reported as having only a 12% rate of progression. 6. Relationships among CLS models: empirical evidence Relatively few studies have attempted to assess the degree of overlap or independence of different CLS models. Sadler-Smith (1997) correlated scores on Reichmann and Grasha’s (1974) SLSS, Honey and Mumford’s (1992) LSQ, Entwistle and Tait’s (1995) RASI, and Riding’s (1991) CSA using samples of UK undergraduate busi- 46 A. Duff / J. of Acc. Ed. 22 (2004) 29–52 ness students (N=245). The largest correlation coefficients were noted between the scales of the RASI and LSQ (deep approach-theorist, r=0.39; strategic approachtheorist, r=0.42) (Sadler-Smith, 1997). However, the magnitude of correlation coefficients between other CLS dimensions is typically slight, indicating that the four instruments are largely measuring different constructs. The findings, taken together with those of Furnham (1996) and Jackson and Lawty-Jones (1996), indicate that the LSQ (and Kolb’s ELM) are likely to be measuring extraversion and deep learning. The importance of both these dimensions to learning helps explain how Kolb’s ELM has been so influential. Extraversion, for example, is likely to determine an individual’s preference for interaction with others in a learning situation, with individuals scoring low on the dimension exhibiting a preference for solitary, individual work rather than group activities. An individual scoring high on the theorist dimension (or abstract conceptualization in Kolb’s ELM), is likely to have a desirable and effective approach to learning, characterized by high scores on deep and strategic approaches. 7. Conclusion and avenues for future research Different groups of researchers seem determined to pursue their own pet distinctions in cheerful disregard of one another. . .In my opinion, the right thing to do is to focus. . .on the search for individual differences which are basic, in the sense that they underlie (and to that extent explain), a whole range of more readily observable differences. (Lewis, 1976, pp. 304–305) This literature review indicates that accounting educators have been keen adopters of elements of cognitive learning style (CLS) research over a significant period of time. Two conclusions that can be drawn from the literature review are that accounting education researchers have not made full use of the CLS literature and associated instruments and that there has been no concerted attempt to explain or synthesize efforts in CLS research within accounting education. Accounting faculty who apply learning styles need to be concerned about confusing definitions, weaknesses in measurement reliability and validity, and identifying relevant characteristics in learners and instructional settings (Curry, 1991). The work of Stout and Ruble (summarized in Ruble & Stout, 1994) on the psychometric properties of the LSI-1976, 1985 demonstrates the importance of using valid instruments to measure accounting students’ CLS. It is unclear why instruments such as the LSI and LSQ are so widely used in accounting education research, although the fact that these measures are free and readily available may account for much of their use. Researchers must, however, be careful to select from the full range of available research instruments those having adequate reliability and validity. This review of the literature also indicates that much CLS research in accounting education lacks a theoretical base. Two models that offer considerable promise to A. Duff / J. of Acc. Ed. 22 (2004) 29–52 47 accounting education researchers are those of Furnham (1995) and Ramsden (1992). Each of these models attempts to link individual characteristics and experience via learning to explain achievement or performance. An important point is that SAL researchers view performance as the ‘quality of learning’ rather than a raw score in a closed-book examination or other assessment. Beattie et al.’s (1997) assertion that accounting attracts a relatively high proportion of reproducing and achieving students implies that accounting educators may perceive a learning approach as a personality trait; that is, something stable and relatively permanent. Empirical evidence suggests, however, that approaches to learning are dynamic and change during the course of a student’s period of study (Zeegers, 2001). Although Ramsden (1992) recognizes that students use differing approaches on different occasions, he notes ‘‘that general tendencies to adopt particular approaches, related to the different demands of course and previous educational experience do exist’’ (p. 51). It therefore remains an interesting empirical question as to which approaches to learning items are stable and which are dynamic.8 An extension of this research question is whether the nature of accounting and its assessment regime, emphasizing closed-book examinations, encourages a surface, reproducing conception of learning compared with other subject areas (Lonka & Lindblom-Ylanne, 1996). More work is needed to consider the practical application of CLS constructs. For instance, is CLS, measured by whatever means, a useful predictor (or moderator) of job-based or academic performance when combined with other predictors, such as personality, cognitive ability, motivation and background variables such as age, gender, or prior educational experience? This paper has made a deliberate distinction between structural models, underlying CIP models, and process models that support SAL research and the 3Ps model. Importantly, these models are complementary with CIP models emphasizing modes of representing, processing, and organizing information and the SAL model considering the nature and philosophy of learning and the quality of learning outcomes. SAL research focuses on creating an educational environment where teaching, learning, and assessment will encourage students to consider alternatives and develop critical thinking skills. CIP research stresses basic individual differences that instructors should: (i) accommodate, in the case of the Verbal–Imagery bipolar dimension, (ii) complement Wholist and Analytic styles, and (iii) develop a balance in their students’ abilities to think intuitively and rationally. To develop students’ learning competencies (learning to learn), accounting educators should make use of both CIP and SAL paradigms. SAL research from its phenomenological roots of the university classroom differentiates between the quality of learning outcomes instead of merely focusing on raw academic performance. The CIP paradigm helps accounting educators understand how the learner represents, organizes and processes information and their capability to address structured 8 For example, personality constructs are generally conceived as being stable (traits) whilst anger, for example, is a state (temporal). Some constructs can exhibit both trait and state. 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