The role of cognitive learning styles inaccounting 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 competenciesis limited by the variety of conceptualizations, constructs and instruments. This paper con-

trasts two models for operationalizing CLS: Furnham’s [Furnham, A. (1995). The relation-ship 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.] contextualmodel of student learning. The origins of CLS, its fundamental dimensions, and methods of

assessment are also reviewed. Five propositions suggesting ways accounting educators canmake use of CLS and associated measures to help students ‘learn how to learn’ are developedand 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 additionto teaching technical material, have been consistently made over the past twodecades (AAA, 1986; AECC, 1990; AICPA, 1997, 1999; Albrecht & Sack, 2001;Williams, 1999). Central to the developing personal competencies is understanding

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0748-5751/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jaccedu.2003.09.004

* Corresponding author. Tel.: +44-1292-886296; fax: +44-1292-886250.

E-mail address: angus.duff@paisley.ac.uk (A. Duff).

accounting students’ learning styles and preferences. Identifying a suitable frame-work for understanding how learning takes place in an accounting context is, how-ever, 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 ofindividual differences in information processing strategies. These consistent cogni-tive differences have been referred to as ‘cognitive learning style’ (CLS) (Witkin,Moore, Goodenough, & Cox, 1977). Accounting educators have been keen adoptersof CLS research. Three principal areas of interest to those applying CLS withinaccounting education are to improve educational effectiveness, to identify differ-ences 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 conceptualmodels that identify the antecedents of CLS. The second part of the paper classifiesCLS models into two categories: (i) cognitive information processing models and (ii)students’ approaches to learning. Within this section we summarize the workundertaken by accounting educators using CLS and identify some potentially fruit-ful avenues for accounting education research. Finally, we discuss some implicationsof 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 mod-erator 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 achieve-ment. Second, personality and intelligence can predict CLS, which in turn is amoderate of academic performance. Third, teaching and assessment methods areindependently 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.

30 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

may not—‘match’ an individual’s preferred CLS.2 Importantly, these researcherstend to draw from cognitive psychology in exploring how learning takes place asstudents 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 asthe 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.

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thinking of educational psychologists from the so-called Student Approaches toLearning (SAL) school, the development of which is described in greater detail laterwithin this paper. SAL researchers see learning as contextually-based and ‘bottom-up’ and criticize traditional theoretical sources, as outlined by Furnham (1995) asbeing ‘top-down’ and ‘acontextual’. Although traditional CLS models, as exemplifiedby Furnham (1995), focus on individuals, SAL researchers claim their instruments arebased on a theoretical rationale grounded in how students actually go about learningtasks 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’sapproach to learning is influenced by perceptions of the task requirements, which inturn are influenced by prior educational experiences and the context for learning (e.g.the curriculum, teaching processes, and assessment). Consequently, SAL researcherstend to play down the role that intelligence (or cognitive ability) and personality mightplay in determining learning outcomes, focusing more on prior educational experienceand the context of learning—which can be directly influenced by educators.

3. Classifying CLS models

As Furnham’s (1995) conceptualization identifies, CLS measures lie somewherebetween aptitude/ability measures and personality measures. CLS has been viewedas 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 relativelypermanent and enduring construct. Consequently, in an educational setting, CLS isseen as a trait of the individual student. When CLS is viewed as a process, the focusmoves to how students’ CLS may be changed. Researchers and practitioners whoutilize process CLS models, see style as dynamic and malleable, creating educationalinterventions which can be used to compensate for weaknesses and build on existingstrengths. Others view CLS as both a process and structure, being relatively stableyet capable of change. The next part of this paper describes specific learning stylemodels and associated measures, provides some evidence of their psychometricproperties, explains how the models have been used by accounting educationresearchers, and discusses the potential for learning style models for guiding futureaccounting education research.

4. Cognitive information processing models

Information processing style can be thought of as an individual’s approach toassimilating information. Sadler-Smith (2002) argues that the heightened interest incognitive information processing in management-related disciplines can be attrib-uted to a recognition that the ability to assimilate, process, and respond to increas-ing amounts of information is a key competency for today’s managers. Examples ofcognitive information processing (CIP) models include: Kolb’s Experiential Learn-

32 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

ing Model (ELM) (1976), Cognitive Styles Analysis (Riding, 1991); Cognitive StylesIndex (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 ofrepresenting information, while the second model addresses the mode of organizingand processing information.

4.1. Kolb’s ELM

Kolb’s ELM describes learning in terms of processes rather than outcomes. In thissense, Kolb’s ELM can be thought of as a cognitive information processing modelconceived as a mode of organizing and processing information. The ELM has fourdistinct stages: (i) concrete experience, (ii) observation and reflection, (iii) formationand generalization of abstract concepts, and (iv) the testing of these concepts in newsituations, leading to further concrete experiences. Each stage can be conceived as alevel of ability, in that an ideal learner has the capacity to operate with equal facilityat all four stages. However, most learners will have a preference for one or morestages in the process (or cycle).Three self-report inventories exist which purport to identify an individual’s pre-

ferred CLS: the Learning Style Inventory (LSI) (Kolb, 1976); a later revision of theLSI (LSI-1985) (Kolb, 1985); and the Learning Styles Questionnaire (LSQ) (Honey& Mumford, 1986, 1992). Both the LSI-1976 and LSI-1985 identify four learningstyles: reflective observation (RO), abstract conceptualization (AC), active experi-mentation (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 DeCiantis & Kirton, 1996; Duff & Duffy, 2002; Swailes & Senior, 2000 for empiricalevidence considering LSQ scores). The LSI has recently undergone revision to createa third version (Kolb, 1999a, 1999b), and two further ELM instruments: the Adap-tive 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, andLSP ‘‘show a high degree of commensurability’’ (p. 22). Research correlating theLSQ with personality measures indicates that the Activist–Reflector bipolar scale isclosely 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 basicdimensions of style’: the Wholist–Analytic (W–A) style dimension of whether an

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34

Table 1

Descriptions and dimensions of cognitive learning style- cognitive information processing models category

Model Description References

Cognitive Styles Analysis (CSA)

Wholist–Analytical The tendency to process information: in parts or as a whole Riding (1991)

Verbalizer–Imager The tendency to for the individual to think in: words, or pictures

Cognitive Styles Index (CSI) Allinson and

Hayes (1996)Reasoning–Intuitive A preference for developing understanding by: using reasoning; or spontaneity/insight

Active–Contemplative A preference for learning activity which allows for active participation, or passive reflection

Kirton Adaptation Index (KAI) Kirton (1976, 1994)

Adaptor–Innovator 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

Learning Styles Inventory (LSI; LSI-1985)

Concrete experience; A model consisting of two bipolar scales emphasizing: abstractness over concreteness

(labelled prehension), and action over reflection, (labelled transformation).

Kolb (1976, 1985)

Reflective observation;

Abstract conceptualization;

Active experimentation.

Learning Styles Questionnaire (LSQ) Honey and

Mumford

(1986, 1992)

Activist 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.

Theorist

Reflector

Pragmatist

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individual tends to process information as a whole or in component parts and theVerbal–Imagery (V–I) style dimension describing whether an individual is inclinedto represent information verbally or in mental pictures.The CSA measures both super-ordinate categories of cognitive information pro-

cessing: 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–Analysisdimension 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. Rid-

ing & Agrell, 1997; Riding & Craig, 1999), further investigation indicates that themeasure has poor internal consistency reliability and test re-test reliability (Peterson,Deary, & Austin, 2003a). Peterson et al. (2003a) indicate the psychometric propertiesof 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 thetest increases the chance of respondent fatigue. Replying to Riding’s (2003) com-mentary on their work, Peterson, Deary, and Austin (2003b) conclude that Riding’scomments ‘‘merely distract from rather than criticise, our simple, novel, positivefinding 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 singledimension of style: Intuition–Analysis, a style dimension conceived as a resultof Allinson and Hayes’ (1988, 1990) earlier work considering the learning styles ofmanagers using the LSQ. Their exploratory factor analytic studies consistently pro-duced two factors that they labelled ‘Analysis’ and ‘Action’. The development of theCSI 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, andanalysis 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-solvingwhich can be described as either intuitive or rational. It is claimed that the Intuitive–Rational dichotomy represents a long established difference in contrasting modesof thought (Nickerson, Perkins, & Smith, 1985). The Intuition–Rational dimensioncan be thought of as a means of organizing and processing information. Intuitionand Rational styles are said to characterize right-brain and left-brain thinking, asAllinson and Hayes (1996 p. 122) suggest:

Intuition, characteristic of the right brain orientation, refers to immediatejudgment based on feeling and the adoption of a global perspective. Analysis,characteristic of the left brain orientation, refers to judgment based on mentalreasoning and a focus on detail. These right-left patterns are not merely tran-

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sient; people seem to have a rather permanent stylistic orientation to the use ofone 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 analytictechniques and correlation with other measures of individual difference providesome 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 associatedwith creativity, problem-solving and decision-making. In turn, these strategies aresaid to relate to simple aspects of personality (traits) that manifest themselves earlyin life. Kirton’s bipolar dimension Adaptation–Innovation is said to be stable overboth time and incident. Adaptors tend to like to ‘do things better’, while an Innovatorhas 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–retestreliability over time periods from 7 to 41 months (r from 0.82 to 0.86), and highconstruct validity in a range of cultural contexts (see Kirton, 1994, pp. 14–19).

4.2. Empirical evidence of relationship between different cognitive informationprocessing instruments

Sadler-Smith (2001) examined the relationship between Kolb’s learning styles(using the LSI) and cognitive style (using the CSA), reporting non-statistically sig-nificant 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 dimen-sions 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 bearssimilarities to that of the Rational–Intuition dimension of the CSI, with empiricalevidence positively relating the KAI to a measure of left–right brain style of thinking(Torrance, Reynolds, Ball, & Riegel, 1978) which underlies the bipolar dimension ofthe 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’ preferredCLS in an accounting education context used the LSI-1976 and considerableresearch using this measure has been conducted by a number of authors, mainly in

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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 acorrelational study sampling UK accounting students (N=142) reported that theLSQ showed unsatisfactory internal consistency reliability, construct validity, andpredictive validity. Sangster (1996) in a study of UK accounting students,5 relatedscores on the LSQ to their preference for assessment using computer-based objectivetesting. Duff (1998) drawing on his experience with using the LSQ and the results ofvalidity studies in other disciplines, indicated that Sangster’s (1996) application ofthe LSQ was premature. Recent work in accounting has also indicated that the LSQproduces scores with unsatisfactory measurement properties and is unsuitable foruse in applied research or observational studies (Duff, 2001a).

Fig. 3. Mindmap (Buzan, 1995) of cognitive information processing models.

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.

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Gul (1986) was the first to apply Kirton’s adaptation–innovation theory toaccounting education. Using a sample of 26 Australian undergraduate students, Gulidentified a majority as being classified in the adaptor, rather than innovator, cate-gory. Wolk and Cates (1994) used Kirton’s (1976) KAI to investigate differencesbetween problem-solving styles of samples of accounting students (N=39) and otherbusiness students (N=118) in the US. Differences between the two samples were notfound, with accounting students more likely to be adaptors than other businessstudents.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 innova-tive style. The authors note that KAI scores were correlated with certain pedagogicalperceptions 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 unstructuredtask. Similar to the findings of Wolk and Cates (1994), the majority (72%) of studentswere classified as adaptors, with the minority (28%) as Innovators. As hypothesized,students classified as innovators outperformed those classified as Adaptors on theunstructured task. However, no statistically significant difference was reportedbetween 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. Researchhas shown that associated measures (LSI-1976, LSI-1985, LSQ) yield scores ofdoubtful validity. Consequently, the ELM is unsuitable for applied research until ameasure 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 inVerbal and Imager styles by the adoption of appropriate instructional and learningstrategies will lead to enhance learning performance and increase the ability of thestudent to ‘learn how to learn’.

For example, instructors accommodate both V–I styles by verbally presenting infor-mation to accommodate a Verbal style and visually presenting information to accom-modate 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.

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responses, making connections without logical thought (Parker & Stone, 2003)could be used to accommodate Verbalizers. Furthermore, if collaborative learning isbeing 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. AWholist may see the so-called ‘big picture’ but struggle to identify structures withinthe overall concept. Likewise, an individual with an Analytic style may understandparticular elements, but have difficulty in seeing how the elements fit together. Tosupport a Wholist style, an instructor might adopt a highly structured route throughlearning, explaining the relationships among concepts, which their global style ofprocessing information may miss. Providing a content overview to illustrate how aparticular concept, which might otherwise be overemphasized, fits into the overallstructure could accommodate an Analytic style. These ideas are captured in propo-sition 2, which is specified as:

Proposition 2. Accounting education that accommodates individual differences inWholist and Analytic styles by the appropriate instructional and learning strategies willenhance 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 learningsituations. Therefore, it is desirable to encourage the individual to develop newstrategies to complement their primary style. Geary and Rooney (1993), reviewingthe contribution accounting teaching materials make to the learning process, notethat accounting education has historically been based on sensate thinking, ratherthan intuitive thinking. Consequently, if accounting educators are to improve theirstudents’ learning competencies, materials that help students develop intuitivethinking skills should be used. Intuitive skills could be developed by exposing stu-dents to more unstructured problems, encouraging the querying of a problem’sconcomitant assumptions, and by presenting a range of paradigms which might cutacross a particular problem or situation.These ideas lead to proposition 3

Proposition 3. Accounting education that accommodates individual differences inAdaptor (Rational) and Innovator (Intuition) styles by the appropriate instructionaland learning strategies will lead to enhance learning performance and increase theability 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 con-trasting approaches to reading academic articles and texts: a ‘deep’ approach and a

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‘surface’ approach. A deep approach entails looking for meaning in the matter beingstudied and relating it to other experiences and ideas. A surface approach can bethought of as a reliance on rote learning and memorization in isolation from otherideas. It is generally held that the development of a deep approach is consistent withthe avowed aims of higher education (Hayes, King, & Richardson, 1997). A deepapproach to learning is likely to result from relevance to students’ interests (Frans-son, 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 learn-ing has been seen as a means of: (i) encouraging a more systematic approach toacademic teaching (Katz & Henry, 1988); (ii) assisting individual academics whowant to monitor and improve the effectiveness of their own teaching (Richardson,1990); (iii) identifying students who are ‘at risk’ because of ineffective study strate-gies (Tait & Entwistle, 1996); (iv) observing the outcomes (Biggs & Collis, 1982) andexperience of learning (Marton, Hounsell, & Entwistle, 1984); and (v) evaluating thequality 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 theApproaches to Studying Inventory (ASI) in the UK, (2) Biggs’ (1978, 1987) StudyProcesses Questionnaire (SPQ) developed in Australia, (3) Vermunt’s (1992) Inven-tory 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 summarizedin 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 con-tinuation of Marton and Saljo’s (1976) concepts of ‘deep’ and ‘surface’ approaches.Entwistle et al (1979) linked students’ preference for instructional methods to theirlevel of information processing when learning; that is, a deep or surface approach tothe 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, surfaceapproach, strategic approach, lack of direction, metacognitive awareness of study-ing, 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 fac-tors for the previously identified deep and surface processing activities. The factorsrelate to intrinsic, extrinsic, and achievement motivation. However, empirical workhas shown the SPQ to have unsatisfactory measurement properties (Christensen,Massey, & Issacs, 1991; Kember & Gow, 1991; Kember, Wong, & Leung, 1999;

40 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

Table 2

Descriptions and dimensions of cognitive learning style- students’ approaches to learning category

Model Description References

Approaches to Studying Inventory (ASI)

Meaning orientation; An individual’s preferred approach to studying, which

makes explicit reference to their instructional preference

Entwistle et al. (1979)

Reproducing orientation;

Achieving orientation;

Holistic orientation.

Inventory of Learning Styles (ILS)

Undirected An individual’s preferred orientation to learning consisting of four dimensions: Vermunt (1992)

Reproduction directed

Application directed

Meaning directed

Inventory of Learning Processes (ILP)

Synthesis-analysis; A framework identifying the ‘quality of thinking’ which occurs during learning

relating to the distinctiveness, transferability and durability of memory

Schmeck et al (1977)

Elaborative processing;

Fact retention;

Study methods.

Study Processes Questionnaire (SPQ)

Surface–deep (achievement orientation); An individual’s preference for approach to studying, including a study motive element Biggs (1978, 1985)

Intrinsic–extrinsic (achievement orientation).

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41

O’Neil & Child, 1984). The instrument has recently undergone revision, reflecting itsdevelopment 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 assess-

ment of approach to learning. The English language version of the instrumentconsists of 100 items in two sections addressing ‘study activities’ and ‘studymotives’. Vermunt distinguishes four learning styles: undirected, reproductiondirected, application directed, and meaning directed. The undirected and reproduc-tion directed styles are approximately equivalent to Entwistle and his co-workersnotion of a surface approach. Application directed and meaning directed stylesare similar to the concept of a deep approach. The four distinct learning styleshave been confirmed using samples of students in traditional university environ-ments (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 ofthinking’. The quality of thinking, is said to affect the distinctiveness, transfer-ability, and durability of memories that result from the learning event (Schmeck,1988). Schmeck et al. (1977) operationalized this theory by the development ofthe Inventory of Learning Processes (ILP), which purports to measure CLS usingfour scales: synthesis-analysis, elaborative processing, fact retention, and studymethods.

5.2. Prior application of SAL research within accounting education

A search of the literature revealed eight published empirical studies of account-ing students’ approaches to learning and two literature reviews (Beattie, Collinsand McInnes, 1997; Lucas, 1996). The empirical investigations utilized threeinstruments: 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 andJoyce (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 inAustralia, the authors report students obtaining high scores on the deep processingand elaborative processing scales significantly outperforming those achieving lowscores on these two subscales. However, Duff (1997a) reported that the ILP had

42 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

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 higherscores for surface approach and lower scores for deep approach, than science andEnglish 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) studentsprogressed through their programme of study, they became more inclined to adopt asurface approach and less inclined to adopt a deep approach. The authors suggestthese findings may be attributable to a number of factors including: excessiveworkload, 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) toexplore the relationship between students’ approaches to learning and their educa-tional background, age, and gender. No main effects were reported in the relation-ship between approaches to learning and students’ educational background.However, mature students (25 years or older) scored higher on deep approach andstrategic approach than their younger counterparts and self-reports indicated thatfemales were more likely than males to adopt a surface approach. Administering a38-item RASI to four cross-sectional samples of professional accounting (CIMA)students (N=547), Hassall and Joyce (2001) reported that surface approach scoresdeclined over the four stages of the CIMA qualification, whilst deep approach scoresremained stable.Booth et al. (1999) compared scores on the SPQ for samples of Australian

accounting undergraduate students (N=374) to previously reported norms forAustralian arts, education, and science students. The accounting students werefound to have relatively higher surface approach and lower deep approach scalescores than did the other student groups. Furthermore, higher surface approachscores 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 per-formance. Although deep approach scores were positively related to performancein complex examination questions, no relationship was reported between deepapproach and performance on less complex questions or mean grades. Surfaceapproach scores were not related to any aspect of academic performance. AlthoughDavidson (2002) did not report the measurement qualities of SPQ scores, therelatively low predictive power of the investigation may reflect the psychometriclimitations 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.

A. Duff / J. of Acc. Ed. 22 (2004) 29–52 43

5.3. Students’ approaches to learning: implications for accounting educators

To summarize, the recent application of the approaches to learning paradigm inaccounting education has been vigorous and has shown some utility to informaccounting educators of how their students learn. The next part of this paperdevelops two propositions to identify how accounting educators could successfullyuse SAL measures and the 3Ps model in their own teaching. These propositionsfocus on improving teaching practice and identifying students ‘at risk’ of failure dueto 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 oflearning outcome is directly related to a student’s approach to learning. The devel-opment of desirable (i.e. deep and strategic) approaches is dependent both on anawareness of a student’s orientation to learning as well as the contextual dependencyof teaching and learning. This leads to proposition 4

Proposition 4. Accounting educators should recognize that the quality of learning isdirectly influenced by students’ approaches to learning, which in turn depend on both anawareness of the contextual dependency of learning and teaching.

Research applying the ASI measure indicates that approaches to learning andassociated learning outcomes may differ among disciplines (Entwistle, 1984; Meyer,Parsons, & Dunne, 1990; Meyer & Watson, 1991). In general, liberal arts studentsare believed to display higher levels of intrinsic interest in their studies and adopt adeep approach, whilst students in vocational disciplines are more motivated byvocational concerns and adopt a surface approach (Ramsden & Entwistle, 1981;Watkins & Hattie, 1981). In this sense, the perceptions and experiences of theteaching 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 instructionaland assessment methods and workload are determined by educators and adminis-trators. Assessment is one of the most important contextual variables that influencesapproach to learning (Tang, 1998). Accounting educators should look to adoptmethods that assess cohesive and structural qualities of learning, rather than discretequantities of knowledge. For example, multiple-choice test questions and essayquestions that are marked to preset answers, with marks awarded for each piece ofcorrect knowledge, encourage rote-learning and memorization strategies; that is, asurface approach to learning. Continually-assessed projects, learning portfolios, andessay questions that encourage students to demonstrate the quality and integrity oftheir 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 oflearning outcomes (see Tang, 1998 for a recent review).

44 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

5.5. Identify students ‘at risk’ due to poor learning strategies

Research applying the ASI generally finds that academic performance is positivelycorrelated with strategic approach and negatively correlated with surface and apa-thetic approaches (Entwistle & Ramsden, 1983). High scores on deep approach arepositively related to academic performance when the assessment procedure directlyfavours the demonstration of conceptual understanding (Entwistle, Tait, &McCune, 2000). Consequently, deep approach and strategic approach are con-ceptually related as components of effective studying, with surface approach nega-tively related to strategic approach. This conception is analogous to Janssen’s (1996)categorization of an effective student—a studax—characterized as employing anapproach of depth and strategy. Conversely, students scoring high on surfaceapproach 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 withineffective study strategies—using SAL measures—which are likely to impair theiracademic performance and increase the probability they will withdraw from theaccounting program.

Cluster analysis has explored the patterns of response using the ASI and latervariants to identify sub-groups which vary in terms of their levels of attainment andbackground (Entwistle et al., 2000; Meyer, 1991; Meyer and Muller, 1990; Meyer etal., 1990). These studies have typically uncovered one persistent low attainmentcluster, 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 feed-back about the results may encourage students to be more self-aware, develop anunderstanding of the determinants for success in accounting programs, and encou-rage them to seek assistance when they encounter difficulty with their studies. Sec-ond, scores on SAL inventories will provide information that instructors can use toidentify ‘at risk’ students. Support and counseling, either on an individual or groupbasis, can then be provided to these students. Duff (2002) reports that a clusteranalysis of 60 UK accounting and business economics first-year (freshman) students’scores on the RASI reveals two clusters, labelled ‘effective learner’ and ‘ineffectivelearner’. The ‘effective learner’ has a 75% rate of progression, defined as scoringa pass in all subjects studied, whilst the ‘ineffective learner’ cluster is reported ashaving 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 indepen-dence of different CLS models. Sadler-Smith (1997) correlated scores on Reichmannand 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-

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ness students (N=245). The largest correlation coefficients were noted between thescales of the RASI and LSQ (deep approach-theorist, r=0.39; strategic approach-theorist, r=0.42) (Sadler-Smith, 1997). However, the magnitude of correlationcoefficients between other CLS dimensions is typically slight, indicating that the fourinstruments are largely measuring different constructs. The findings, taken togetherwith those of Furnham (1996) and Jackson and Lawty-Jones (1996), indicate thatthe LSQ (and Kolb’s ELM) are likely to be measuring extraversion and deep learn-ing. The importance of both these dimensions to learning helps explain how Kolb’sELM has been so influential. Extraversion, for example, is likely to determine anindividual’s preference for interaction with others in a learning situation, with indi-viduals scoring low on the dimension exhibiting a preference for solitary, individualwork rather than group activities. An individual scoring high on the theoristdimension (or abstract conceptualization in Kolb’s ELM), is likely to have a desir-able and effective approach to learning, characterized by high scores on deep andstrategic approaches.

7. Conclusion and avenues for future research

Different groups of researchers seem determined to pursue their own pet dis-tinctions in cheerful disregard of one another. . .In my opinion, the right thingto do is to focus. . .on the search for individual differences which are basic, inthe sense that they underlie (and to that extent explain), a whole range of morereadily observable differences. (Lewis, 1976, pp. 304–305)

This literature review indicates that accounting educators have been keen adoptersof elements of cognitive learning style (CLS) research over a significant period oftime. Two conclusions that can be drawn from the literature review are thataccounting education researchers have not made full use of the CLS literature andassociated instruments and that there has been no concerted attempt to explain orsynthesize efforts in CLS research within accounting education.Accounting faculty who apply learning styles need to be concerned about confus-

ing definitions, weaknesses in measurement reliability and validity, and identifyingrelevant characteristics in learners and instructional settings (Curry, 1991). Thework of Stout and Ruble (summarized in Ruble & Stout, 1994) on the psychometricproperties of the LSI-1976, 1985 demonstrates the importance of using valid instru-ments 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 andreadily available may account for much of their use. Researchers must, however, becareful to select from the full range of available research instruments those havingadequate 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

46 A. Duff / J. of Acc. Ed. 22 (2004) 29–52

accounting education researchers are those of Furnham (1995) and Ramsden (1992).Each of these models attempts to link individual characteristics and experience vialearning to explain achievement or performance. An important point is that SALresearchers view performance as the ‘quality of learning’ rather than a raw score in aclosed-book examination or other assessment.Beattie et al.’s (1997) assertion that accounting attracts a relatively high propor-

tion of reproducing and achieving students implies that accounting educators mayperceive a learning approach as a personality trait; that is, something stable andrelatively permanent. Empirical evidence suggests, however, that approaches tolearning are dynamic and change during the course of a student’s period of study(Zeegers, 2001). Although Ramsden (1992) recognizes that students use differingapproaches on different occasions, he notes ‘‘that general tendencies to adopt parti-cular approaches, related to the different demands of course and previous educa-tional experience do exist’’ (p. 51). It therefore remains an interesting empiricalquestion as to which approaches to learning items are stable and which aredynamic.8 An extension of this research question is whether the nature of account-ing and its assessment regime, emphasizing closed-book examinations, encourages asurface, 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) ofjob-based or academic performance when combined with other predictors, such aspersonality, cognitive ability, motivation and background variables such as age,gender, or prior educational experience?This paper has made a deliberate distinction between structural models, under-

lying CIP models, and process models that support SAL research and the 3Psmodel. Importantly, these models are complementary with CIP models emphasizingmodes of representing, processing, and organizing information and the SAL modelconsidering the nature and philosophy of learning and the quality of learning out-comes. SAL research focuses on creating an educational environment where teach-ing, learning, and assessment will encourage students to consider alternatives anddevelop critical thinking skills. CIP research stresses basic individual differences thatinstructors should: (i) accommodate, in the case of the Verbal–Imagery bipolardimension, (ii) complement Wholist and Analytic styles, and (iii) develop a balancein their students’ abilities to think intuitively and rationally.To develop students’ learning competencies (learning to learn), accounting edu-

cators should make use of both CIP and SAL paradigms. SAL research from itsphenomenological roots of the university classroom differentiates between the qual-ity of learning outcomes instead of merely focusing on raw academic performance.The CIP paradigm helps accounting educators understand how the learner repre-sents, 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. For example, some com-

ponents of anxiety are a trait, but others temporary (brought about by an event such as an examination).

A. Duff / J. of Acc. Ed. 22 (2004) 29–52 47

and unstructured problems. Only when accounting educators have a firm grasp ofCLS concepts can prescriptive calls for developing accounting students’ learningcompetencies be realized.

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