Exploring Style: Enhancing the capacity to learn 9781846638411, 9781846638404

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ISSN 0040-0912

Volume 50 Number 2 2008

Celebrating 50 years

Education + Training Exploring style: enhancing the capacity to learn? Guest Editors: Carol Evans and Martin Graff

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Education + Training

ISSN 0040-0912 Volume 50 Number 2 2008

Exploring style: enhancing the capacity to learn? Guest Editors Carol Evans and Martin Graff

Access this journal online ______________________________

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Editorial advisory board ________________________________

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Guest editorial ___________________________________________

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Cognitive styles and managerial behaviour: a qualitative study Eva Cools and Herman Van Den Broeck ___________________________

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Learning styles across cultures: suggestions for educators Zarina M. Charlesworth _________________________________________

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Emotional competence and drop-out rates in higher education Emma Kingston _______________________________________________

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Trainee teachers’ cognitive styles and notions of differentiation Carol Evans and Michael Waring _________________________________

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CONTENTS

CONTENTS continued

Implementation of learning styles at the teacher level Tine Nielsen __________________________________________________

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Using concept mapping to measure learning quality David Hay and Ian Kinchin ______________________________________

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EDITORIAL ADVISORY BOARD

John Berkeley Centre for Lifelong Learning, University of Warwick, UK

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Professor Tom Bourner Centre for Management Development, University of Brighton, UK Amanda Cahir-O’Donnell Managing Director, TIO Consulting, Ireland Professor Thomas Clarke University of Technology, Sydney, Australia Helen Connor Institute of Employment Studies, UK Thomas Cooney Dublin Institute of Technology, Dublin, Ireland Professor Dr Thomas Dessinger University of Konstanz, Germany Bruce Dodge Nova Scotia Department of Labour, Canada Professor Karen Evans Institute of Education, University of London, UK Professor Alison Fuller Reader in Education, School of Education, University of Southampton, UK Dr John Goodwin Senior Lecturer and Sub-Dean for Graduate Studies (Social Sciences), Centre for Labour Market Studies, University of Leicester, UK Professor Paul D. Hannon National Council for Graduate Entrepreneurship, Birmingham, UK Dr Chandana Jayawardena Professor and Coordinator, Graduate Program in Hospitality & Tourism, Niagara College, Ontario, Canada

Education + Training Vol. 50 No. 2, 2008 p. 92 # Emerald Group Publishing Limited 0040-0912

Professor Ewart Keep SKOPE, Cardiff, UK Rod Kenyon Director, British Gas Engineering Academy, Berkshire, UK Professor Harry Matlay University of Central England, UK Liz Rhodes Director, NCWE, Oxfordshire, UK Professor Simon Roodhouse University of the Arts, London, UK Dr Eric Sandelands Director, Corporate Learning Consultants, UK Sue Shaw Executive Head of Human Resource Management and Organisation Behaviour, Manchester Metropolitan University Business School, UK Dr Vikki Smith Director, Assessment and Quality, City & Guilds, UK Professor Alan Smithers Brunel University, UK Professor Stefan Wolter Swiss Coordination Centre for Research in Education, Switzerland Professor Adrian Ziderman Department of Economics, Bar-Ilan University, Israel

Guest editorial About the Guest Editors Carol Evans is currently an Education Adviser at the Kent, Surrey and Sussex Postgraduate Deanery, University of London, she is moving to become Deputy Director of Learning and Teaching at the Institute of Education, University of London. Prior to teaching at Durham University before this, she worked in schools for over 22 years in a variety of roles from classroom teacher to senior manager. Her research interests include: improving conditions for learning in the classroom and teacher education programmes; and cognitive and learning styles. She is also an Honorary Fellow of Durham University and Vice President of the European Learning Styles Information Network (ELSIN). [email protected] Martin Graff is a Reader in psychology at the University of Glamorgan, and an Associate Fellow of the British Psychological Society. He has published in the field of computer-based learning and cognitive style and has also carried out consultancy work for numerous education authorities. He is currently an executive member of ELSIN, the European Learning Styles Information Network. [email protected]

Context The potential impact of learning and cognitive styles research on learning in education and the workplace continues to be a fiercely debated one (Davidson, 1990; Lawrence, 1997; Hall et al., 2004). While here may be increasing attention to government driven personalisation agenda within mainstream education in the UK as part of their pursuit for lifelong learning, pragmatic on-going issues continue to be raised in relation to how an understanding of cognitive and learning styles can be applied in a meaningful and effective way in both school and workplace settings (Coffield et al., 2004). Fundamental questions also remain regarding the relative merits of various cognitive/learning styles measures, along with issues to do with the relative impact of cognitive and learning styles on learning when compared to a myriad of factors affecting the learner both directly and indirectly in the creation of favourable learning environments. The selection of papers presented here from the 12th Annual European Learning Styles Information Network Conference (held in June 2007 at the School of Computer Sciences and Statistics, University of Dublin, Trinity College) consider developments in the field of individual differences in learning (ILDs) which are directly applicable to education and training environments. The key themes identified include: . the predictive potential of various style tools and the relevance of such information acquired so as to inform planning and design of training in education and workplace settings; . the use and development of specific techniques such as concept mapping to elicit understandings, encourage shared understandings and communities of practice and facilitate curriculum re-design; . the employment of tools to explore differences in approaches to learning used by different learners so as to inform the design of training and facilitate a better understanding of cultural differences in learning; and . the design of programmes to heighten teacher/instructor understanding of individual differences in learning so as to promote programmes which are more attuned to both the individual needs of the learner and those of the organisation.

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These selected studies respond to Rayner’s (2006) timely call for an increasingly more functional research agenda that takes more account of practitioner awareness and applications of cognitive style. This is essential if an informed understanding is to be shared more widely within and among training environments. Within this vein, there is an increasing move towards small-scale practitioner-led action research projects away from larger studies in relation to potential impact (Claxton, 2007). UK context Considering the performance levels of many children within the UK (for example, 20 per cent of 11-year-olds leave primary school functionally illiterate and less than 50 per cent of 16-year-olds achieve five GCSE passes including maths and English (Woodhead, 2007)), it can be argued that many children are in receipt of a “pedagogy of poverty” rather than one of plenty (Tomlinson, 2005). So despite those announcements of a renaissance of a learning age in Britain such as those in 1998 (DfEE, 1998) there remains a large section of the population who have still not tuned into the learning age or culture (Aldridge and Tuckett, 2007). NIACE (Aldridge and Tuckett, 2007) have concluded that: “the learning society that all European industrial societies aspire to – a society in which everyone is a confident learner and active citizen – remains a long way out of reach” (Kingston, 2007, p. 1). In addition to this the Leitch Report highlights a skills crisis within the UK (DIUS, 2007). Consequently, the UK Government’s drive for 50 per cent engagement in Higher Education within the UK (currently 44 per cent) is problematic. Simpson (2007) acknowledges that the risk of drop out from British Universities is approximately 20 per cent in the first year. Millar and Griffiths (2007) identify that if the government’s target of 50 per cent engagement is reached without any subsequent improvement in the drop out rate, each year approximately 10 per cent of 18 year olds will endure the experience of quitting before the end of their courses. Given the increasing heterogeneous nature of those involved in Higher Education and the need for more differentiated instruction to accommodate this, larger student numbers have actually worked against greater personalisation of teaching. In addition, it has also been estimated that approximately 20 per cent of today’s UK HE students undertake some form of workplace learning as part of their courses compared to 48% of their European contemporaries (Brennan and Little, 2007) again suggesting an impoverished model for many. In a climate of increasingly informal, independent and internet related learning activities (Aldridge and Tuckett, 2007), a key goal and challenge is to “foster students” abilities to integrate learning over time, across courses, and between academic, personal and community life . . . ” (Shulman, 2004) and thereby encompass a more holistic view of education. Such an integrated approach, encouraging a breadth and depth of understanding is increasingly recognised as important for brain development (Sandy, 2008). More flexible learning pathways for all learners is required. Consequently an understanding of how learning can be more attuned to the needs of individuals is essential. In affecting such a change, Vermunt (2007) advocates the development of a pedagogy of teacher learning whereby changes in teaching methods are required to accommodate a student’s increasing levels of self regulation. Such pedagogy makes increasing demands on both learners and instructors requiring learners to be more involved in the process of learning and for teachers to be more aware of individual

learning differences (ILDs) as they construct and negotiate learning environments (DfES, 2006). In making such a proposition, Vermunt (2007) argues the need for a “super model” to incorporate affective, social, biological and environmental components. He is not alone in wanting greater unification of constructs in order to better understand individual learning differences. Zhang and Sternberg place cognitive style with other style constructs under the umbrella heading of “intellectual styles”. This they define as “one’s preferred way of processing information and dealing with tasks . . . [it is] . . . to varying degrees . . . cognitive, affective, physiological, psychological, and sociological . . .” Zhang and Sternberg (2005, p. 2). The broad range of constructs comprising the study of personal individual learning differences including gender, personality, intelligence and abilities, self-reference, cognitive styles, learning styles and motivation/attitude formation is also commented on by Rayner (2007). Such a super model needs to consider all aspects that make up a learning profile. As Nielson argues in this edition such a model of change needs to include cognitive, motivational and epistemological factors, as well as the connections between them in order to fully mobilise learning potential. In addition the model needs to be clarify the interrelationships between aspects so as to minimise isolation and diffusion of knowledge (Vermunt, 2007). In assisting individuals to develop the “capacity to learn”, (Claxton, 2007), the way in which cognitive and learning styles approaches encourage learners to consider the processes of learning can be very powerful (O’Malley and Charmot, 1990; Evans and Waring, 2006; Rosenfeld and Rosenfeld, 2004). This is dependent on how such approaches are implemented and crucially how they are discussed in context. Developing an understanding of style(s) The broad, all encompassing and complex nature of “styles” continues to make the transfer of information into workplace and educational contexts difficult. For example, cognitive styles are typically seen as more habitual than learning styles which are viewed in the literature as being more adaptable and context related. Traditional notions of styles as traits which are intransigent and inflexible (Allport, 1937; Messick, 1984; Schmeck, 1988; Atkinson, 2004) are being challenged. Current thinking emphasises the flexibility of style (Vermunt, 2007) and is moving away from the use of traditional terms such as cognitive style to discussions of intellectual styles (Zhang and Sternberg, 2005), dispositions and individual learning differences (Rosenfeld and Rosenfeld, 2007) and learning patterns (Vermunt, 2007). (The distinction made between states and traits in that the former can be changed and the latter is more stable is not unproblematic as identified by Zhang and Sternberg (2005, p. 34) when they note styles “can normally be rather stable, except when there is a demand for change of styles by specific situations . . .”). Preferring “learning pattern” over “style(s)” as a descriptor of individual differences in learning, Vermunt (2007) argues that patterns have the potential to develop over time and to vary across contexts. As such they can be socialised and modified as a “function of the interaction of person, task, and situation” (Zhang and Sternberg, (2005, p. 34). Such potential mobility of style holds great promise for educators in that it should be possible for individual educators to modify and adapt their styles if they are

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cognisant of them and have support and training to develop a wider styles repertoire (Evans and Waring, 2006; Rosenfeld and Rosenfeld, 2007). The independence of styles versus the context specific nature of them continues to raise a number of questions. For example, to what extent are the knowing, planning and creating styles of Cools and Van Den Broeck mutually exclusive and context dependent? Do people with different cognitive styles approach management roles differently as suggested by Cools and Van Den Broeck? Are certain styles better for the demands of certain roles and how does this concur/conflict with issues of authenticity in management behaviour in relation to developing one’s own style in accordance with personality and character? In addition, does the way in which different cognitive styles approach conflict and feedback situations resemble their preferred way of decision making or is this too simplistic? Further complexity is added when investigating causal relationships such as this, especially when the extent of the relationship between one’s own style and one’s observed behaviours may not always be clear or a sole function of cognitive style (Evans, 2004). Furthermore, if cognitive styles influence the tasks people prefer the most in the work environment, as a manager/trainer do you allow individuals to stay within their comfort zones or encourage them to develop broader approaches? In using the information we have on intellectual styles, the key value as identified in the selection of papers presented here is in their training potential, enabling trainers to work with students/employees to identify areas of strength and areas to develop as part of their own professional and holistic development. With this in mind, Vermunt (2007) points to the necessity and importance of teaching and training interventions to enable modifications in learning patterns, but also acknowledges in order to effect positive change, strong and powerful learning environments are required. The challenge therefore becomes the definition of such environments and what they encompass so as to be able to clearly identify how they can enhance an individual’s/organisation’s capacity for learning. Learning about style Zhang and Sternberg (2005) acknowledge that although purported to be non-pejorative styles have never been value free. The relative currency of one cognitive style over another, it could be argued, varies temporally and spatially and at the individual organisational level. Consequently, that which might be positively valued in one learning environment may not be in another. The on-going question still remains as to which styles to use and how best to use them for training purposes. There are many cognitive style tools with differing names, some of which measure similar and others very different aspects of style (Evans and Sadler-Smith, 2006). In an attempt to provide a clearer route map Rayner (2000) employed the term “learning profile” to represent an umbrella concept to include cognitive style, learning style, learning strategies, preferences, motivation and self perception. How useful this was is debateable. Moving the field forward, Vermunt (2007) suggests the need for an integrated learning instrument to include existing and affective elements. However, Rayner (2007) questions whether greater agreement on a super-ordinate structure of style is required to establish the key priorities in styles research. Another attempt to clarify the style horizon has been made by Zhang and Sternberg (2005, p. 2) with their development of

“intellectual styles” defined as “One’s preferred way of processing information and dealing with tasks . . . [it is] . . . to varying degrees . . . cognitive, affective, physiological, psychological, and sociological”. In attempting to unify the various intellectual styles encompassed by such a definition they argue that any style may have one or more of the following concepts as part of its underpinnings: preference for “high degrees of structure versus low degrees of structure, for cognitive simplicity versus cognitive complexity, for conformity versus non-conformity, for authority versus autonomy and for group versus individual work.” They consider these to be key areas that should be addressed in designing training programmes. Developing powerful learning environments From the papers presented in this special edition, four key aspects highlight themselves as important in developing powerful learning environments. These are: (1) the need to involve learners more centrally in the process of learning and subsequent development and use of integrated tools to assist this; (2) developing a pedagogy of learning styles; (3) enhancing teacher sensitivity to ILDs to inform planning, delivery and assessment of learning; and (4) the essential development of a coherent research agenda to underpin such work. Involvement of learners Regardless of the uncertain verdict regarding the debate over the stability of styles, we do know that a student’s involvement in the learning process has a direct, positive and significant effect on academic achievement (Betoret, 2006) and that this is most likely to be achieved in an environment where teachers create a climate for learning which considers individual differences (Opdenakker and Van Damme, 2006). Several of the papers presented in this special edition suggest a greater need for a “scholarship of teaching and learning”, whereby students study the learning process and the conditions under which learning is most likely to occur for themselves and others. By giving learners a greater voice in pedagogical matters and enabling them to develop their own metacognitive capabilities they would be enabled to be better learners by making connections between what is learned in very different, and typically unconnected settings (Hutchings, 2007). In order to design courses to match the needs of learners Vermunt (2007) suggests the need for an enhanced understanding of the new student and the ways in which technological developments have lead to different patterns of processing among members of the mobile generation. A generation to whom multi-tasking and self-directed learning have become more prevalent. Similarly, the application of technological advancements could be used more effectively to design new learning environments attuned to those ILDs. A pedagogy of learning In attending to ILDs, a key issue is the development of a pedagogy of intellectual styles that is used sensitively, constructively and critically to inform the learner, so as to enable the pursuit of self-directed learning. Vermunt (2007) argues the need to adapt teaching to certain design principles in order to promote more favourable learning

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patterns in real settings. Consequently he advocates the customisation of teaching in order to cultivate student learning, with workplace learning as a key contributor to such a pedagogy. Increased development of informal learning opportunities, flexible pathways and peer support also need to be realised. Such an informed pedagogy with the learner clearly situated at the centre would make learning explicit using specific tools to unearth an understanding in the development of new communities of practice. In their paper Hay and Kinchin show how the use of concept mapping can be used to assess prior learning and to identify differences in understanding among students and teachers in both education and workplace settings. An envisioned “pedagogy of plenty” would attend to the diverse needs of the learning population taking account of the interplay of intellectual styles with other mediators such as culture (Charlesworth); school context and levels of experience (Evans and Waring; Nielson); prior learning (Hay and Kinchin); type of organisation (Cools and Van Den Broeck); affective elements (Kingston). Affective aspects of the learning process also need to be considered, Kingston and Nielson in their papers each argue that an analysis of these with both students, employees and those responsible for organising learning in higher education and employment may facilitate a better understanding of the learning process to be developed. Kingston, referring to Riding (2002) highlights the association between negative affect and impairment of working memory capacity. The key questions here are how can we encourage positive affect in order to expand individual potential for and of learning? Should training programmes place greater emphasis on the emotional aspects of learning, as well as consider the extent to which cognitive and affective elements of learning are interrelated? Addressing change at the emotional level is essential (Patrick and Pintrich, 2001) because the attitudes (of leaner and instructor) are more resistant to change if the emotional component of the attitude is unmodified in conjunction with the cognitive component, as discussed in the Nielson paper. Teacher development In order to make the move from a “pedagogy of poverty” to a “pedagogy of plenty” (Tomlinson, 2005) while catering for the increasing diversity of student learning needs, effective teachers will need to be aware of and use a variety of teaching styles (Kulinna and Cothran, 2003). To do this Nielson argues for more training time to be devoted to how to utilise the information about style(s) in order to apply them more effectively to practice. There is growing evidence that suggests instructional interventions aimed at enhancing teacher awareness of their own cognitive styles and the ways in which such styles impact on classroom practices are enabling teachers to be more aware of their own learning and that of others (Dunn et al., 1995; Lawrence, 1997; Riding and Watts, 1997; Heffler, 2001; Coffield et al., 2004; Hall et al., 2004; Rosenfeld and Rosenfeld, 2004, 2007; Evans and Waring, 2006), and in so doing helping them to plan for differentiation of learning more effectively (Evans and Waring, 2007). The effects of such interventions have been shown to have lasting impacts on teachers’ attitudes to individual student needs and practice (Nielson, 2007; Rosenfeld and Rosenfeld, 2007). The adoption of a metacognitive approach has also been advocated whereby an individual is encouraged to analyse their own learning approaches in order to enhance teacher sensitivity to ILDs as discussed in the papers by Nielson, Evans & Waring and

Kingston. Significant work in this area has been carried out by Rosenfeld and Rosenfeld (2007, p. 283) who identify four key principles in the development of teacher sensitivity to ILDs: (1) favouring a constructivist approach in which teachers examine their own belief systems about individual learning; (2) favouring a collaborative approach fostered through the creation of a safe and supportive learning environment; (3) teachers required to recognise and actively increase their repertoire for addressing diverse learner individual learning differences in themselves, colleagues and students; and (4) reinforcing feedback loop for the teachers based on the increased success of their students and other learners enabling the teachers to move from pathognomonic (blame-the-learner) beliefs about students to a broader understanding about how the student learns and what the teacher can do to intervene. Developing a coherent research agenda Research activity needs to support coherence and consensus in style theory. Rayner (2007, p. 296) asks a key question: “Do we need to develop new forms of research as part of a paradigm shift and a consensual epistemology for style differences research?” However, research in this area is dominated by positivist concepts and an essentialist ontology. Constructivist psychology, phenomenological and practitioner evidence-based enquiry is much needed to (re)assert the theoretical integrity of styles research. Rayner (2007) and Vermunt (2007) both acknowledge that work on intellectual styles still fails in delivery and impact on practice. The integration of research and practice is essential. Research into individual learning differences has implications for instructors in teacher education programmes and for educators providing professional development opportunities in the workplace. Continuing professional development programmes should be designed to promote the more effective use of styles and in so doing will enable teachers/instructors to use a wider variety of styles and in such ways develop instructor understanding of teacher pedagogical knowledge. Also, in looking to the future, teachers and teacher education programmes need to consider which teaching styles are best suited to the needs of the individual (Kulinna and Cothran, 2003, p. 9) and develop increased criticality in the value and limitations of style applications. However, attempts to isolate variables that determine teachers’ preferred teaching style have to date revealed very little about teachers’ use and perception of various teaching styles (Kulinna and Cothran, 2003), or the stability of such teaching styles (Evans, 2004). Much more research is needed in these areas. Looking to the future The papers in this special edition have considered ways in which an understanding of style(s) and associated tools can be used to enhance the learning process through a metacognitive approach. The degree of stability of style(s) is debated with little consensus evident. However, it is clear that style(s) can be modified through the adoption of appropriate strategies. More research is required to verify such findings and to agree on “an established definition of style difference securely located within

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differential psychology as a concept, construct and meaning. . . [and to consider through the richness afforded by qualitative study] the ‘. . .pedagogic implications of style differences in instruction and training’” (Rayner, 2007, p. 296). Carol Evans and Martin Graff Guest Editors

100 References Aldridge, F. and Tuckett, A. (2007), Road to Nowhere? Survey on Adult Participation in Learning, National Institute of Adult Continuing Education (NIACE), Leicester. Allport, G.W. (1937), Personality: A Psychological Interpretation, Holt & Co, New York, NY. Atkinson, S. (2004), “A comparison of pupil learning and achievement in computer aided learning and traditionally taught situations with special reference to cognitive style and gender issues”, Educational Psychology, Vol. 24 No. 5, pp. 659-72. Betoret, F.D. (2006), “Testing an instructional model in a university educational setting from the student’s perspective”, Learning and Instruction, Vol. 16, pp. 450-66. Brennan, J. and Little, B. (2007), “Liberation by degrees”, Work based Learning, Times Higher Education Supplement, 11 May, pp. 6-7. Claxton, G. (2007), “Expanding young people’s capacity to learn”, British Journal of Educational Studies, Vol. 55 No. 2, pp. 115-34. Coffield, F., Moseley, D., Hall, E. and Ecclestone, K. (2004), Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review, Learning and Skills Research Centre (LSDA), London. Davidson, G.V. (1990), “Matching learning styles with teaching styles: is it a useful concept in instruction?”, Performance and Instruction, Vol. 29, pp. 36-8. DfEE (1998), The Learning Age: A Renaissance for a New Britain, Department for Education and Employment, The Stationery Office, London. DfES (2006), 2020 Vision, Report of the Teaching and Learning in 2020 Review Group, Department for Education and Skills, Nottingham. DIUS (2007), Prosperity for the Global Economy: World Class Skills, Department for Innovation, Universities & Skills, London. Dunn, R., Griggs, S.A., Olson, J., Beasley, M. and Gorman, B.S. (1995), “A meta-analytic validation of the Dunn and Dunn model of learning style preference”, Journal of Educational Research, Vol. 88, pp. 353-62. Evans, C. (2004), “Exploring the relationship between cognitive style and teaching style”, Educational Psychology, Vol. 24 No. 4, pp. 509-30. Evans, C. and Sadler-Smith, E. (2006), “Learning styles”, Education + Training, Vol. 48 Nos 2-3, Special Issue. Evans, C. and Waring, M. (2006), “Towards inclusive teacher education: sensitising individuals to how they learn”, Educational Psychology, Vol. 26 No. 4, pp. 499-518. Evans, C. and Waring, M. (2007), “A comparison of the cognitive styles and notions of differentiation amongst trainee teachers”, in Redmond, J.A., Parkinson, A., Moore, C., Stenson, A., Evans, C., Rayner, S., Armstrong, S., Graff, M., Lassen, L., Bostrom, L., Peterson, E. and Ashwin, A. (Eds), Exploring Style: Enhancing the Capacity to Learn?, Proceedings of the 12th Annual Conference of the European Learning Styles Information Network, Trinity College, Dublin, Ireland, pp. 165-77.

Hall, E., Moseley, D., Ecclestone, K. and Coffield, F. (2004), “Researching learning styles”, Teaching Thinking, Spring, pp. 28-35. Heffler, B. (2001), “Individual learning style and the learning style inventory”, Educational Studies, Vol. 27 No. 3, pp. 307-16. Hutchings, P. (2007), Building Pedagogical Intelligence, The Carnegie Foundation for the Advancement of Teaching, available at: www.carnegiefoundation.org/perspectives/sub. asp?key ¼ 245&sukey ¼ 571 (accessed 30 October 2007). Kingston, P. (2007), “School’s out – for some”, Lifelong Learning, The Guardian, 22 May, p. 1. Kulinna, P.H. and Cothran, D.J. (2003), “Physical education teachers’ self-reported use and perceptions of various teaching styles”, Learning and Instruction, Vol. 31 No. 6, pp. 597-609. Lawrence, M.V.M. (1997), “Secondary school teachers and learning style preferences: action or watching in the classroom?”, Educational Psychology, Vol. 17 No. 1 and 2, pp. 157-70. Messick, S. (1984), “The nature of cognitive styles: problems and promise in educational practice”, Educational Psychologist, Vol. 19 No. 2, pp. 59-74. Millar, P. and Griffiths, S. (2007), “University: who needs it?”, The Sunday Times, 1 January, p. 1. Nielson, T. (2007), “Implementation of learning styles in adult teaching: a suggestion for an approach”, in Redmond, J.A., Parkinson, A., Moore, C.A., Stenson, A., Evans, C., Rayner, S., Armstrong, S., Graff, M., Lassen, L., Bostrom, L., Peterson, E. and Ashwin, A. (Eds), Exploring Style: Enhancing the Capacity to Learn?, Proceedings of the 12th Annual Conference of the European Learning Styles Information Network, pp. 91-101. O’Malley, J. and Charmot, A. (1990), Learning Strategies in Second Language Acquisition, Cambridge University Press, Cambridge. Opdenakker, M.C. and Van Damme, J. (2006), “Teacher characteristics and teaching styles as effectiveness enhancing factors of classroom practice”, Teaching and Teacher Education, Vol. 22, pp. 1-21. Patrick, H. and Pintrich, P.R. (2001), “Conceptual change in teachers’ intuitive conceptions of learning, motivational instruction: the role of motivational and epistemological beliefs”, in Torf, B. and Sternberg, R. (Eds), Understanding and Teaching the Intuitive Mind, Student and Teacher Learning, Lawrence Erlbaum, London, pp. 117-44. Rayner, S. (2000), “Reconstructing style differences in thinking and learning: profiling learning performance”, in Riding, R. and Rayner, S. (Eds), International Perspectives on Individual Differences, Vol. 1, Ablex, Stamford, CT. Rayner, S. (2006), “What next? Developing global research and applied practice in the field of cognitive and learning styles”, in Lassen, L., Bostrom, L. and Evans, C. (Eds), Enabling Lifelong Learning in Education, Training and Development, Proceedings of the 11th Annual Conference of the European Learning Styles Information Network, University of Oslo, Oslo (CD-ROM). Rayner, S. (2007), “Whither style differences research – global paradigm or knowledge diaspora?”, in Redmond, J.A., Parkinson, A., Moore, C., Stenson, A., Evans, C., Rayner, S., Armstrong, S., Graff, M., Lassen, L., Bostrom, L., Peterson, E. and Ashwin, A. (Eds), Exploring Style: Enhancing the Capacity to Learn?, Proceedings of the 12th Annual Conference of the European Learning Styles Information Network, Trinity College, Dublin, Ireland, pp. 293-6. Riding, R.J. (2002), School Learning and Cognitive Style, David Fulton Publishers Ltd, London. Riding, R.J. and Watts, M. (1997), “The effect of cognitive style on the preferred format of instructional material”, Educational Psychology, Vol. 17 Nos 1 and 2, pp. 179-83.

Guest editorial

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Rosenfeld, M. and Rosenfeld, S. (2004), “Developing teacher sensitivity to individual learning differences”, Educational Psychology, Vol. 24 No. 4, pp. 465-87. Rosenfeld, M. and Rosenfeld, S. (2007), “Developing effective teacher beliefs about learners: the role of sensitising teachers to individual learning differences (ILDs)”, in Redmond, J.A., Parkinson, A., Moore, C., Stenson, A., Evans, C., Rayner, S., Armstrong, S., Graff, M., Lassen, L., Bostrom, L., Peterson, E. and Ashwin, A. (Eds), Exploring Style: Enhancing the Capacity to Learn?, Proceedings of the 12th Annual Conference of the European Learning Styles Information Network, Trinity College, Dublin, Ireland, pp. 268-92. Sandy, M. (2008), “Sex, chocolate and better, brainier world”, Times Higher Educational Supplement, 4 January, p. 8. Schmeck, R.R. (Ed.) (1988), Styles and Strategies of Learning, Plenum, New York, NY. Shulman, L. (2004), Integrative Learning Project Summer Institute, July 2004, available at: www. carnegiefoundation.org/files/elibrary/integrativelearning/index (accessed 30 October 2007). Simpson, O. (2007), “Strike it rich by drilling into the vulnerable core”, Times Higher Education Supplement, 8 June, p. 14. Tomlinson, C.A. (2005), “Differentiated instruction as a way to achieve equity and excellence in today’s schools, building inclusive schools: a search for solutions”, Conference Report, Canadian Teachers’ Federation Conference, Ottawa, Ontario, 17-19 November 2005, pp. 19-21. Vermunt, J. (2007), “Student learning and teacher learning”, keynote address at The European Learning Styles Information Network, 12th Annual Conference: Exploring Style: Enhancing the Capacity to Learn, Trinity College, Dublin, 12-14 June 2007. Woodhead, C. (2007), “Now a cloak of invisibility falls on school standards”, The Sunday Times, 14 January, p. 17. Zhang, L.F. and Sternberg, R.J. (2005), “A threefold model of intellectual styles”, Educational Psychology Review, Vol. 17 No. 1, pp. 1-53. Further reading Desmedt, E. and Valcke, M. (2004), “Mapping the learning styles ‘jungle’: an overview of the literature based on citation analysis”, Educational Psychology, Vol. 24 No. 4, pp. 445-64. Leonard, N.H., Scholl, R.W. and Kowalski, K.B. (1999), “Information processing style and decision making”, Journal of Organizational Behavior, Vol. 20 No. 3, pp. 407-20. Sternberg, R.J. (1997), Thinking Styles, Cambridge University Press, Cambridge.

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0040-0912.htm

Cognitive styles and managerial behaviour: a qualitative study

Cognitive styles and managerial behaviour

Eva Cools People and Organisation Department, Vlerick Leuven Gent Management School, Gent, Belgium, and

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Herman Van Den Broeck Vlerick Leuven Gent Management School and Ghent University, Gent, Belgium Abstract Purpose – The purpose of this paper is to contribute further insights into how cognitive styles influence managerial behaviour, using a qualitative approach. Design/methodology/approach – Written testimonies were gathered from people with different cognitive styles, and content analysed (n ¼ 100). Findings – Qualitative evidence was found for managerial style preferences in accordance with cognitive styles, leading to various ways of decision making, conflict handling, and giving feedback. Research limitations/implications – Future research should explore how these results can be linked to contextual elements and to managerial performance. Practical implications – This study contributes to increased managerial style awareness, which is important for intrapersonal development and interpersonal cooperation. Originality/value – This is one of a few studies that have sought to qualitatively grasp the implications of having a particular cognitive style. It provides relevant insights into task- and people-oriented managerial practices beyond previous, mainly quantitative studies. Keywords Management styles, Qualitative research, Organizational behaviour Paper type Research paper

Introduction In addition to situational factors, individual characteristics play an important role in determining managerial performance (Stevens and Ash, 2001). According to Berr et al. (2000), there is at this moment considerable interest in the potential impact of individual dispositions on managerial behaviour and effectiveness. Cognitive styles may not be ignored in this regard, as they are expected to influence how people develop their managerial role (Buttner et al., 1999). Research has shown that cognitive style differences influence learning, problem solving, decision making, communication, interpersonal functioning, and creativity in important ways (Hayes and Allinson, 1994; Kirton, 2003; Sadler-Smith, 1998). Cognitive styles are believed to be a crucial factor for effective decision making and for successful interpersonal cooperation (Armstrong and Priola, 2001). The aim of this study is to examine the link between cognitive styles and managerial behaviour, using a qualitative approach. The uniqueness of the study lies in the qualitative approach that was chosen to address this issue and in the combined focus on task-oriented and people-oriented managerial behaviour. This way, this research wants to refine and extend findings from previous, mainly quantitative studies on the relation between cognitive styles and particular aspects of managerial behaviour. Although there is widespread empirical interest in cognitive styles,

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qualitative studies that provide further support to the practical relevance of cognitive styles for organisations are currently lacking (Rayner, 2006). With the increased prevalence of executive coaching and the use of managerial assessment, research on the impact of individual differences on managerial behaviour is highly relevant (Berr et al., 2000).

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Conceptual framework Cognitive styles A cognitive style has been defined as the way in which people perceive stimuli and how they use this information for guiding their behaviour (Hayes and Allinson, 1998). Cognitive styles have gained prominence in the education and management literature over the last decades (Hayes and Allinson, 1994, 1998). Traditionally, cognitive style research has focused mainly on the distinction between analytical and intuitive thinking (Hodgkinson and Sadler-Smith, 2003). However, empirical research on the relation between different cognitive style measures found that cognitive style is a complex variable with multiple dimensions (Leonard et al., 1999). Recently, Cools and Van den Broeck (2007) reported on the development of a reliable, valid, and convenient multidimensional cognitive style instrument – the Cognitive Style Indicator (CoSI) – for use with managerial and professional groups. They demonstrated the relevance and usefulness of identifying three cognitive styles (Table I). People with a knowing style are characterised by a preference for facts and details, while people with a planning style prefer structure and order, and people with a creating style tend to proliferate ideas and to like experimentation. Cools and Van den Broeck (2007) found substantial support for this instrument’s construct validity by including other cognitive style instruments, and personality and academic performance measures in the validation process. The managerial role As managerial positions encompass a wide range of activities (Magretta, 2002; Mintzberg, 1994), management has been defined as “the process of working with and through others to achieve organisational objectives in an efficient and ethical way” (Kreitner et al., 2002, p. 8). This definition implies a task-oriented (achieving goals) and a people-oriented aspect (working with and through others). Whereas early management theories have focused on task issues, contemporary models increasingly value the human aspect (Kouzes and Posner, 2002).

Table I. Description of the CoSI model

Knowing style

Planning style

Creating style

Facts Details Logical Reflective Objective Impersonal Rational Precision

Sequential Structured Conventional Conformity Planned Organised Systematic Routine

Possibilities Ideas Impulsive Flexible Open-ended Novelty Subjective Inventive

Note: Based on Table 1 in Cools and Van den Broeck (2007)

The aim of this research was not to derive an exhaustive list of the activities of managers, but rather to focus on how they perform their roles. According to Lamond (2004), there has recently been more attention on how managers execute their tasks. Lamond (2004) made an interesting distinction between enacted managerial styles (i.e. actual behaviour) and preferred managerial styles (i.e. preferences people have regarding their roles). There is no consensus regarding which factors constitute this managerial style. This study focused on preferred managerial styles, using cognitive styles as the distinguishing factor. In line with the above definition of management, task and people-oriented practices were involved in this research. With regard to the task-oriented aspects, the study focused on decision making, as this is an important informational aspect of the managerial role that might be influenced by cognitive style differences (Leonard et al., 1999). Previous quantitative research found that people prefer decision-making processes that are compatible with their cognitive style (Galle´n, 2006; Hough and Ogilvie, 2005). For the people-oriented aspects, this study focused on conflict handling and giving feedback, as these are two important managerial tasks (Kouzes and Posner, 2002). Research evidence suggests that cognitive style differences may fundamentally affect interpersonal relationships (Armstrong, 2000). Researchers found, for instance, relationships between people’s preferred ways of information processing and their styles of handling interpersonal conflicts (Johnson, 1997). Research design To grasp the managerial implications of having a knowing, planning, or creating style, a qualitative approach seemed warranted. Qualitative research leads to a better understanding of the meaning of what is observed and results in data of greater depth and richness (Patton, 2002). Despite the call for more qualitative research in organisational behaviour and management studies (e.g. Gephart, 2004; Symon et al., 2000), there is still a lack of qualitative studies on cognitive styles. Recently, Rayner (2006) stated that there can be no doubt that the psychometric tradition and positivist paradigm dominate the cognitive style research domain. He calls for more functional research that takes practitioner awareness and applications of cognitive styles into account. By using a qualitative approach, this study wanted to contribute to these calls for an increased focus on the relevance of cognitive style research for practice. In sum, decision making, information processing, and dealing with people are important aspects of effective management (Tett et al., 2000). As cognitive styles are individual preferences with regard to how people perceive, think, learn, solve problems, and relate to others (Witkin et al., 1977), looking at the influence of cognitive style differences on managerial behaviour is highly relevant. Based on previous (mainly quantitative) research findings, people with different cognitive styles are expected to approach their management role differently. Methodology Procedure and sample Management and MBA students were invited to write a testimony (with choice as to content and organisation) on how they typically behaved in an organisational context as part of an assignment. In addition, they completed the 18-item cognitive style indicator (CoSI) (Cools and Van den Broeck, 2007). Item and factor analyses confirmed

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the three-dimensional cognitive style model, with Cronbach alpha coefficients in this study being 0.78, 0.83, and 0.77 for the knowing, planning, and creating style respectively. A total of 553 testimonies were collected in total, including both 275 management and MBA students of a leading Western European business school, as well as a convenience sample of 278 employees. Using the procedure adopted by Butterfield et al. (1996), employee data were collected through the students who each contacted one employee. Of all the respondents, 63 per cent were men and 37 per cent were women, both in the student and in the employee sample. Their age ranged from 21 to 67 years, with a mean age of 30.41 (SD ¼ 10.93). The mean age of the employee sample, however, was higher than the mean age of the student sample and it contained a broader range of ages (M ¼ 38.31 years, SD ¼ 11.35 versus M ¼ 23.22 years, SD ¼ 1.59 respectively). From the 275 business school students, 19 per cent attended an MBA, whereas the others were graduate students who attended a one-year full-time management education. The 278 employees performed a wide range of functions in diverse sectors and represented various hierarchical levels. For further analyses, out of the 553 testimonies completed, those with the most “extreme” profile were selected (i.e. people who scored more than 1 SD above the mean for one of the cognitive styles). In this sense, sampling was based on theoretical considerations instead of randomness to have clear examples of cognitive style differences (Patton, 2002). This way, 100 testimonies were actually used in the final sample (16 people with a knowing style, 41 with a planning style, and 43 with a creating style), containing testimonies from 57 management and MBA students and from 43 employees. Coding and analyses A three-stage content analytic procedure was used, distinguishing a unitising, categorising, and classifying stage (Neuendorf, 2002). (1) The written testimonies were entered into the qualitative software package ATLAS.ti. Units for analysis were paragraphs in each testimony that dealt with separate managerial behaviour. (2) As recommended by other scholars (Potter and Levine-Donnerstein, 1999), a scheme was developed to code the data. Three categories were distinguished: task-oriented behaviours (i.e. paragraphs on decision making, communication, and problem solving), people-oriented aspects (i.e. quotes on teamwork, interpersonal behaviour, conflict handling, and giving feedback), and self-awareness (i.e. quotes in which people indicated the tasks they (dis)like most in their job, and their strengths and weaknesses). (3) Finally, a cross-case analysis was carried out. For each of the codes, the different cognitive styles were compared. Results The study led to a rich amount of information on how people with various cognitive styles prefer to perform certain aspects of their managerial role. On the basis of the qualitative data, an image of managerial characteristics for each of the cognitive styles was built (see Table II).

Motto Attracted by Searches for

Knowing style

Planning style

Think before you act Facts, logic, rationality Accuracy

Plan before you act Cre-act Structure, plans, control Ideas, possibilities Certainty Renewal

Task-oriented behaviour Decision making Detailed analysis Take their time Strengths Analytical skills Logical reasoning Weaknesses Creativity People-oriented behaviour Conflict handling Rational, direct way Based on rational and logical arguments Feedback Rational, straightforward approach Emphasise negative over positive feedback Strength Reliable Weaknesses Too straightforward Lack of empathy “Selling” ideas

Creating style

Structured analysis Quick process Organising skills Sticking to agreements Unforeseen changes

Intuitive analysis Fast process Strong imagination Thinking out-of-the-box Implementation of ideas

Rational, diplomatic way Quick solutions Direct, diplomatic approach

Mainly emotional way Assertive, sometimes even provocative Direct, constructive approach

Both positive and negative feedback Dutiful Demanding to oneself and others Too controlling

Emphasise positive over negative feedback Flexible Difficulty compromising Impulsive

Knowing style Regarding task-oriented behaviour, the analyses show that people with a knowing style like an analytical approach. They want to make informed decisions on the basis of facts and figures, using logical and rational arguments. They prefer to take their time to make decisions, sometimes postponing them to collect more information. A lack of data or relevant information can be a source of doubt for knowing people in the decision-making process. People with a knowing style consider their analytical skills and their logical reasoning as their major strengths. However, this is sometimes also seen as a disadvantage, as they have more difficulties with finding creative, out-of-the-box solutions. People with a knowing style do not like tasks that seem to serve no purpose, that are undefined, intellectually not challenging, and that lack supporting facts and figures. Regarding people-oriented behaviour, people with a knowing style preferably interact with others in a straightforward, rational way. Rational and logical arguments are the basis of acting in conflict situations as well. People with a knowing style also like to give feedback in line with their strengths, preferring a rational and straightforward approach. They are inclined to give more negative than positive feedback, as they find it more useful to give people ways to improve their weaknesses instead of just praising them. Accordingly, a weakness that several knowing people have mentioned is that they are sometimes too focused on rationality and logic when interacting with others leading to a lack of empathy and difficulties in explaining and “selling” their ideas.

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Table II. Managerial characteristics of different cognitive styles

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Planning style People with a planning style also prefer an analytical approach to deal with tasks. They do not like to make decisions on the basis of “gut-feeling”. However, the analyses indicate that planning types are, in their decision making, less focused on facts and figures (compared to people with a knowing style), but prefer above all a structured approach. They try to be quick decision makers in order to shorten the uncertainty that surrounds the decision-making process, as this confronts them with many doubts. People with a planning style report strong organising and planning skills. They feel uncomfortable with uncertainty, unexpected changes, and strategic reorientations. Accordingly, planning types like tasks that involve a planned, organised, and methodical approach that lead to concrete results. People with a planning style preferably interact more in a rational than in an emotional way with others, but they are also concerned with diplomacy. In this sense, they are less focused on rationality as opposed to people with a knowing style. People with a planning style prefer a calm, direct, honest, and diplomatic approach when dealing with conflicts. If a conflict occurs, they want to handle it as soon as possible. Like people with a knowing style, they mostly prefer to solve conflicts through open discussion. Similarly, people with a planning style like to give feedback in a direct, straightforward, and diplomatic way. They give both positive and negative feedback. They find positive feedback important to motivate people. They also have no problem providing negative feedback to give people the chance to improve. Creating style People with a creating style tend to make decisions primarily on the basis of intuition or “gut-feeling”, using objective information and data only in a second phase. They describe decision making as a mixture of an intuitive and rational process. People with a creating style do not doubt much when making decisions and even if they do, it does not prevent them from fast decision making. People with a creating style have a strong imagination and are good at developing new ways of doing things. Accordingly, they prefer tasks which require creativity, action, flexibility, and own input. The weaknesses that are reported by creating types are related to their strength in imagination, as they sometimes keep on suggesting original ideas without considering their possible implementation. Regarding people-oriented behaviour, the analyses show that people with a creating style use either an emotional or a rational approach when interacting with others. Some people tend to use a rational approach to solve conflicts (i.e. staying calm, listening to the different opinions, searching for consensus), while others are more emotionally involved. In general, people with a creating style assertively try to persuade and convince others of their ideas. However, they can change their minds when others are convincingly enough and provide good arguments. People with a creating style prefer to give feedback in a direct and honest way, although they attach a lot of importance to being positive and constructive to make people feel good and to stimulate their self-esteem. People with a creating style report that they have a personal approach in giving feedback, adapting it according to the situation or the person they have to deal with. Discussion and conclusion The unique contribution of this study lies in the qualitative approach that was used to grasp the implications of cognitive style differences on managerial behaviour.

Recently, several scholars emphasised the need for more qualitative research on cognitive styles to better understand what it implicates to have a certain cognitive style (Rayner, 2006; Riding, 2000). On the basis of content analyses of 100 written testimonies, differences in preferred management styles for people with a knowing, planning, and creating style were identified, both for various task-oriented and people-oriented managerial practices. This study refines and extends previous quantitative results on the link between cognitive styles and managerial behaviour. Additionally, this study provides qualitative evidence for the usefulness of distinguishing between the three cognitive styles of the CoSI model (Cools and Van den Broeck, 2007) contrary to the dichotomous thinking in several other cognitive style models (Allinson et al., 2001; Kirton, 2003). Discussion of findings The qualitative analyses showed that people with a knowing and planning style tend to make decisions in an analytical way (although they emphasise different elements), whereas people with a creating style combine an intuitive and rational approach. Creating types do not mind making decisions based on gut-feeling, whereas knowing and planning people try to base their decisions on data and information. Knowing and creating types tend to be mainly focused on the content of decision making (taking facts-based or creative decisions respectively), whereas planning people mostly refer to the decision-making process as such. People with a knowing style like to take their time to make decisions, while people with a planning and creating style prefer quick decision making. Overall, these results strengthen and refine quantitative studies that found that people with different cognitive styles show different decision-making behaviour (Hough and Ogilvie, 2005; Leonard et al., 1999). Previous quantitative research with the CoSI model found that individuals with a knowing style preferred a logical, rational, and impersonal decision-making approach, whereas planning types favoured an objective, structured, conventional, and efficient problem-solving approach, and creating people had a preference for a creative, unconventional, flexible way of decision making (Cools and Van den Broeck, 2007). On the basis of the above findings, it can be concluded that the way in which people with different cognitive styles approach conflict and feedback situations resembles their preferred way of decision making. People with a knowing and planning style both prefer a rational and straightforward interaction approach. Planning types, however, are more inclined to handle conflicts and to give feedback in a diplomatic way, whereas knowing types purely focus on the rationality and logic of the situation. People with a creating style tend to be more emotionally involved, using a personal approach in handling conflicts and giving people feedback. These results confirm that cognitive styles influence how people relate to others, as has been shown in quantitative style research. Previous studies found that people with an analytical style tended to be more task oriented, relatively less friendly, more impersonal, and more self-controlling in their emotional behaviour. Intuitive people, on the contrary, have been shown to be more interpersonally oriented, expressive, relatively friendly, warm towards others, and serving more psychosocial functions during interpersonal relationships (for an overview of these findings, see Armstrong, 2000; Priola et al., 2004). Cognitive styles also seem to influence which tasks people prefer most in their job. People mostly like tasks that make use of their preferred way of perceiving and processing

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information. Leonard and Straus (1997) also found that people tend to develop those areas in their jobs they like and that they try to avoid those aspects they dislike. Given the largely ill-defined nature of the managerial role, part of managerial work is determining its own boundaries (Tett et al., 2000). The findings of this study can be useful in this regard, as cognitive styles influence the tasks people will emphasise most in their job.

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Research implications This study was a first step in the direction of enhanced qualitative understanding of cognitive style differences. However, to increase the relevance and rigour of these findings, further research will be needed combining both quantitative and qualitative (i.e. mixed-method) approach. Lamond (2004), for instance, developed a quantitative instrument to measure enacted and preferred managerial styles – the managerial style measure (MSM) – that can be useful to enhance and strengthen the qualitative findings from this research project. A necessary next step will also be observing and interviewing people in organisations. This study was based on written testimonies, without taking into account contextual factors. A wide variety of people were involved in the study, but differences with regard to level and function could not be taken into account in the analyses. Recently, much attention has been devoted to the importance of the organisational context in organisational studies (Johns, 2006). Additionally, by integrating organisational context elements in future research, it will also be possible to take managerial effectiveness into account. There is currently considerable interest in the assessment of managerial performance and the development of managerial competency models (Tett et al., 2000). This study has not examined the influence of preferred managerial styles on effectiveness. A next logical step will be to link it to performance. Furthermore, it can also be of interest to study managerial styles from the perspective of co-workers (subordinates, peers, supervisors), as they are in a unique position to provide valuable behavioural assessments for two reasons (Berr et al., 2000). On the one hand, colleagues are often affected by the consequences of the focal person’s actions. On the other hand, they can observe this behaviour over time and in a variety of situations. Practical implications According to George (2003), to be authentic in your management behaviour means that you have to develop your own style in accordance with your personality and character. Whetten et al. (2000) emphasised the importance of intrapersonal skills for effective management. This means in their perspective developing self-awareness on the basis of a thorough analysis of one’s strengths and weaknesses. Understanding the interplay between people’s preferences and their day-to-day workplace behaviour is crucial for designing and implementing effective individual development efforts (Berr et al., 2000; Riding and Rayner, 1998). As cognitive styles are considered to be fairly stable characteristics of people (Clapp, 1993), this does not imply changing one’s style, but rather learning about the consequences of having a particular style. People can be trained to adopt strategies to overcome the weaknesses of their styles in specific situations (Armstrong and Sadler-Smith, 2006; Hough and Ogilvie, 2005). In this regard, some relevant action points were identified to enhance managerial style awareness (see Table III).

Learn to balance your direct and rational style with more emotional connection

Empathy: learn to be less demanding to yourself and others. Open up to other approaches, even if you would have done it differently Relax! Let yourself go from time to time, just enjoy

Flexibility and change: learn to be more open to unforeseen situations and innovations as not everything can be planned beforehand Action! Stop planning, rethinking the planning, restructuring the planning of the planning: focus and go for it Stimulate your creativity: learn to think more out-of-the-box, give ideas a chance

Task-oriented behaviour Speed of decision making: do not try to gather all possible information. Speed is as important as the quality of a decision Effective Decision ¼ Quality £ Acceptance: work on “selling” your decision to make sure people are convinced that it is the “right” one Stimulate your creativity: do not directly ask for proof, give ideas a chance

People-oriented behaviour Empathy: not everyone thinks in the same rational, logical way as you. Learn to understand other people’s “logic”

Planning style

Knowing style

Be open to the ideas of others. Listen. Do not impose your ideas

Empathy: learn to have understanding for people who need more details or procedures as you need them to implement your ideas

Project finalisation: commit yourself to both the conceptualisation and the implementation phase of projects Effective Decision ¼ Quality £ Acceptance: check the underlying facts before moving on with an idea Balance your creativity: check your ideas for their feasibility with someone else

Creating style

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Table III. Increasing one’s style awareness: practical implications

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Importantly, no style is inherently better than another. Schroder (1994), for instance, found that cognitive styles are independent of management competence, but do influence the way in which management competence is expressed. Understanding the implications of cognitive style differences can be a basis for fostering better working relationships (Allinson et al., 2001). Overlooking the impact of cognitive differences can lead to interpersonal disagreements and conflict situations, as people with different cognitive styles may not understand or respect each other. Thus, to be successful, effective managers they should be aware of their own cognitive style and those of the people that surround them. George (2003) saw dealing with different types of people as an important developmental task for managers. Managers can increase their effectiveness by working collaboratively with people with various cognitive styles and paying attention to different point of views, attitudes, behaviours, perspectives, and actual cognitions (Riding and Rayner, 1998). References Allinson, C.W., Armstrong, S.J. and Hayes, J. (2001), “The effects of cognitive style on leader-member exchange: a study of manager-subordinate dyads”, Journal of Occupational and Organizational Psychology, Vol. 74 No. 2, pp. 201-20. Armstrong, S.J. (2000), “Individual differences in cognitive style and their potential effects on organizational behavior: a summary of recent empirical studies”, in Riding, R.J. and Rayner, S.G. (Eds), International Perspectives on Individual Differences, Vol. 1, Cognitive Styles, Ablex, Stamford, CT, pp. 215-37. Armstrong, S.J. and Priola, V. (2001), “Individual differences in cognitive style and their effects on task and social orientations of self-managed work teams”, Small Group Research, Vol. 32 No. 3, pp. 283-312. Armstrong, S.J. and Sadler-Smith, E. (2006), “Cognitive style and its relevance for the management of careers”, paper presented at the 66th Academy of Management Conference, Atlanta, GA, 11-16 August. Berr, S.A., Church, A.H. and Waclawski, J. (2000), “The right personality is everything: linking personality preferences to managerial behaviors”, Human Resource Development Quarterly, Vol. 11 No. 2, pp. 133-57. Butterfield, K.D., Trevino, L.K. and Ball, G.A. (1996), “Punishment from the manager’s perspective: a grounded investigation and inductive model”, Academy of Management Journal, Vol. 39 No. 6, pp. 1479-512. Buttner, E.H., Gryskiewicz, N. and Hidore, S.C. (1999), “The relationship between styles of creativity and managerial skills assessment”, British Journal of Management, Vol. 10 No. 3, pp. 228-38. Clapp, R.G. (1993), “Stability of cognitive style in adults and some implications: a longitudinal study of the Kirton Adaption-Innovation Inventory”, Psychological Reports, Vol. 73 No. 2, pp. 1235-45. Cools, E. and Van den Broeck, H. (2007), “Development and validation of the cognitive style indicator”, The Journal of Psychology: Interdisciplinary and Applied, Vol. 141 No. 4, pp. 359-87. Galle´n, T. (2006), “Managers and strategic decisions: does the cognitive style matter?”, Journal of Management Development, Vol. 25 No. 2, pp. 118-33. George, B. (2003), Authentic Leadership, Jossey-Bass, San Francisco, CA.

Gephart, R.P. Jr (2004), “Qualitative research and the Academy of Management Journal”, Academy of Management Journal, Vol. 47 No. 4, pp. 454-62. Hayes, J. and Allinson, C.W. (1994), “Cognitive style and its relevance for management practice”, British Journal of Management, Vol. 5 No. 1, pp. 53-71. Hayes, J. and Allinson, C.W. (1998), “Cognitive style and the theory and practice of individual and collective learning in organizations”, Human Relations, Vol. 51 No. 7, pp. 847-71. Hodgkinson, G.P. and Sadler-Smith, E. (2003), “Complex or unitary? A critique and empirical re-assessment of the Allinson-Hayes Cognitive Style Index”, Journal of Occupational and Organizational Psychology, Vol. 76 No. 3, pp. 243-68. Hough, J.R. and Ogilvie, D.T. (2005), “An empirical test of cognitive style and strategic decision outcomes”, Journal of Management Studies, Vol. 42 No. 2, pp. 417-48. Johns, G. (2006), “The essential impact of context on organizational behaviour”, Academy of Management Review, Vol. 31 No. 2, pp. 386-408. Johnson, A.K. (1997), “Conflict-handling intentions and the MBTI: a construct validity study”, Journal of Psychological Type, Vol. 43 No. 1, pp. 29-39. Kirton, M.J. (2003), Adaption-Innovation in the Context of Diversity and Change, Routledge, New York, NY. Kouzes, J.M. and Posner, B.Z. (2002), The Leadership Challenge, 3rd ed., Jossey-Bass, San Francisco, CA. Kreitner, R., Kinicki, A. and Buelens, M. (2002), Organizational Behaviour, 2nd European ed., McGraw-Hill, London. Lamond, D. (2004), “A matter of style: reconciling Henri and Henry”, Management Decision, Vol. 42 No. 2, pp. 330-56. Leonard, D. and Straus, S. (1997), “Putting your company’s whole brain to work”, Harvard Business Review, Vol. 75 No. 4, pp. 111-21. Leonard, N.H., Scholl, R.W. and Kowalski, K.B. (1999), “Information processing style and decision making”, Journal of Organizational Behavior, Vol. 20 No. 3, pp. 407-20. Magretta, J. (2002), What Management Is: How It Works and Why It’s Everyone’s Business, Profile Books, London. Mintzberg, H. (1994), “Rounding out the manager’s job”, Sloan Management Review, Vol. 36 No. 1, pp. 11-26. Neuendorf, K. (2002), The Content Analysis Guidebook, Sage, Thousand Oaks, CA. Patton, M.Q. (2002), Qualitative Research & Evaluation Methods, 3rd ed., Sage, Thousand Oaks, CA. Potter, W.J. and Levine-Donnerstein, D. (1999), “Rethinking validity and reliability in content-analysis”, Journal of Applied Communication Research, Vol. 27 No. 3, pp. 258-84. Priola, V., Smith, J.L. and Armstrong, S.J. (2004), “Group work and cognitive style: a discursive investigation”, Small Group Research, Vol. 35 No. 5, pp. 565-95. Rayner, S. (2006), “What next? Developing global research and applied practice in the field of cognitive and learning styles”, in Lassen, L., Bostrom, L. and Evans, C. (Eds), Enabling Lifelong Learning in Education, Training and Development, Proceedings of the 11th Annual Conference of the European Learning Styles Information Network, University of Oslo, Oslo (CD-ROM). Riding, R.J. (2000), “Cognitive style: a strategic approach for advancement”, in Riding, R.J. and Rayner, S.G. (Eds), International Perspectives on Individual Differences, Vol. 1, Cognitive Styles, Ablex, Stamford, CT, pp. 365-77.

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Riding, R. and Rayner, S. (1998), Cognitive Styles and Learning Strategies: Understanding Style Differences in Learning and Behaviour, Fulton, London. Sadler-Smith, E. (1998), “Cognitive style: some human resource implications for managers”, International Journal of Human Resource Management, Vol. 9 No. 1, pp. 185-202. Schroder, H.M. (1994), “Managerial competence and style”, in Kirton, M.J. (Ed.), Adaptors and Innovators: Styles of Creativity and Problem Solving, Routledge, New York, NY, pp. 91-113. Stevens, C.D. and Ash, R.A. (2001), “Selecting employees for fit: personality and preferred managerial style”, Journal of Managerial Issues, Vol. XIII No. 4, pp. 500-17. Symon, G., Cassell, C. and Dickson, R. (2000), “Expanding our research and practice through innovative research methods”, European Journal of Work and Organizational Psychology, Vol. 9 No. 4, pp. 457-62. Tett, R.P., Guterman, H.A., Bleier, A. and Murphy, P.J. (2000), “Development and content validation of a ‘hyperdimensional’ taxonomy of managerial competence”, Human Performance, Vol. 13 No. 3, pp. 205-51. Whetten, D., Cameron, K. and Woods, M. (2000), Developing Management Skills for Europe, 2nd ed., Pearson Education, Harlow. Witkin, H.A., Moore, C.A., Goodenough, D.R. and Cox, P.W. (1977), “Field-dependent and field-independent cognitive styles and their educational implications”, Review of Educational Research, Vol. 47 No. 1, pp. 1-64. About the authors Eva Cools, PhD earned a KU Leuven Masters degree in Pedagogical Sciences in 2000 and graduated as a Doctor in Applied Economics at Ghent University in September 2007. She works as a researcher in the People and Organisation Department at Vlerick Leuven Gent Management School, Belgium. Her current research activities focus on cognitive styles, team research, change management, and entrepreneurship. Eva Cools is the corresponding author and can be contacted at: [email protected] Herman Van Den Broeck, PhD, is partner of Vlerick Leuven Gent Management School, where he is head of the People and Organisation Department. He is a professor at the Faculty of Economics and Business Administration of Ghent University and teaches Educational Interaction and Communication at the Teacher Training department.

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Learning styles across cultures: suggestions for educators

Learning styles across cultures

Zarina M. Charlesworth Glion Institute of Higher Education, Bulle-en-Gruye`re, Switzerland

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Abstract Purpose – This paper seeks to present research findings on the relationship between culture and learning styles, as defined by Honey and Mumford in a Higher Education setting. Design/methodology/approach – The research was conducted with first semester students studying in an International Institute of Higher Education. A questionnaire administered to students (n ¼ 113) of Indonesian, Chinese and French origin was analysed in order to compare their learning style preferences. This was followed by a detailed item-by-item analysis of their responses to the same questionnaire. Findings – In the first instance, the data support a relationship between learning styles preferences and cultural background at the outset of a programme of Higher Education. Subsequent analysis provides insight into the nature of these differences. Research limitations/implications – The generalizability of the research findings is limited owing to the nature of the sample. Practical implications – Educators in both Higher Education and business settings can draw on these research findings. It is suggested that allowing incoming students to explore learning style differences will enhance their understanding of how they go about learning as well as possibly influence their learning outcomes. Parallels have been drawn with incoming international employees. Originality/value – These findings have relevance for educators, both in Higher Education and in industry, concerned with how to best develop international graduates and managers. Keywords Learning styles, Culture, Higher education Paper type Research paper

Introduction The research presented here examines the learning styles, as defined by Honey and Mumford (1986) of first semester students studying in an international institute of Higher Education. Taken from a larger multi-cultural sample (n ¼ 315), (Charlesworth, 2005), a sub-sample (n ¼ 113) of students of Indonesian, Chinese and French origin was analysed. The findings provide strong support for a culture-learning style connection. The two analyses presented also provide the reader with some detail as to what differences are being found and suggestions are made for educators wishing to take such preferences into account. The influence of culture on learning styles Reported learning styles research having a cultural component is quite limited. To compound this, the use of different instruments makes any comparative analysis between research findings rather tenuous. What seems to be a frequent lack of rigour in the sampling makes it even more difficult to draw any conclusions as it is not always clear to what extent any socio-economic variables have been examined nor whether the individuals compared are of culturally different backgrounds or simply of different

Education þ Training Vol. 50 No. 2, 2008 pp. 115-127 q Emerald Group Publishing Limited 0040-0912 DOI 10.1108/00400910810862100

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nationalities. Overall, the literature is rather non-committal when it comes to how culture might influence learning styles. Despite this, it seems clear that there must be a relationship between culture and learning. If one accepts that culture is “a certain commonality of meaning, customs and rules (not a homogeneous entity) shared by a certain group of people and setting a complex framework for learning and development” (Trommsdorf and Dasen, 2001, p. 3004) then one cannot deny the connection between culture and learning. The world in which an individual develops provides one with tools needed to adapt. These tools are appropriated through a process of enculturation within which schools play an integral part (Crahay, 1999). Furthermore systematic differences found in the way in which classrooms function in different parts of the world can be largely linked to cultural differences (Crahay, 1996). The processes involved in constructing, acquiring and transforming knowledge and the differences in these processes can be seen as coming from the way in which the learning environment and textbooks are interpreted by the student (Newman et al., 2003). According to Reuchlin (1991), each individual when faced with a learning task might have more than one way of going about it and the selection of one or another method can be linked to a difference between individuals. One would expect then to find differences between individuals no matter what their culture. It is of interest, however, to see whether in addition to such individual differences a cultural component can be shown to be having a demonstrable influence on an individual’s choice in the way in which he/she goes about their learning. One way of examining this is by looking at learning styles of international students. .

Learning in context Anthropologists and sociologists, as well as theorists in other fields, are coming to see that “learning cannot be separated from the contexts in which it occurs, and to re-conceptualise cognition and learning as activities that occur through social interaction” (Lattuca, 2002, p. 711). Researchers who have focussed on the importance of the social and contextual influences on learning (Lave, 1997; Lave and Wenger, 1991; Rogoff, 1990) have spoken of situated learning or situated cognition and above all have “cast learning as a fundamentally social and cultural activity and contrast sharply with behavioural and cognitive models in which learning is conceptualized as an individual activity and as an artefact that can be easily separated from the contexts in which it takes place” (Lattuca, 2002, p. 712). This socio-cultural view also gives equal weight to the influence of both the immediate setting as well as to the larger one in which it is embedded, shifting the focus away from the individual and allowing for a multi-dimensional view of learning. Applying this to learning styles allows them to be seen to be both as individual yet influenced by the social context in which they have developed. The learning environment in Asia and most particularly in China is authoritarian and expository (Ngwainmbi, 2004), using mainly didactic methods and with a focus on cooperative learning. This learning environment seems to be the only one that the Chinese students are exposed to right from the pre-school years where class size is between 40 and 60 and compliance is expected (Corwin, 2001). From the time that one starts school, according to Corwin, undivided attention to the teacher seems to be the norm with disciplinary problems, at all levels, being very rare. Certainly at the primary level free, unstructured time is not part of the school learning environment in China

(2001), and this seems to be the case all the way through to the end of secondary education. Despite an intent to refocus Chinese pedagogy following a redirection of educational policy in 1979 through the planned introduction of a more student-centred focus (Dooley, 2001), the importance of exam results has meant that the teacher-centred focus is still largely present. It would seem that even today, rote learning, memorization and repetition still characterize Chinese education. Similarly the Indonesian educational system is described as one that does not encourage independent, critical thought or learning but rather one that demands regard for and deference to authority figures (Meyer and Kiley, 1998). As in many parts of Asia, group welfare often comes before that of the individual and a concern for harmony is evident. What is termed a “group culture” by Lewis (1997) is evidenced in the Indonesian classroom in the manner in which students avoid setting themselves apart from the group and “tend not to initiate discussion or ask questions unless they are speaking on behalf of the group” (Lewis, 1997, p. 16). Perhaps as a result of this behaviour, the view has become rather widely held that Indonesian students are passive learners and this is reinforced by the manner in which the Indonesian classroom operates with the teacher occupying a position of authority and deference (Novera, 2004). The French classroom is one that has, historically a very traditional structure. Although the reformed school system dating back to the 1880s (Fowler and Poetter, 2004) was subject to change following the Second World War, the idea that “school was to be the means of instituting, of anchoring deeply and securely, the ‘one and indivisible’ Republic” (Lelie`vre, 2000, p. 7) is still more than just lingering in the system. A system which even today is nationalised and characterised by a focussed and conceptually tight curriculum (van Zanten, 2002). Despite this the pedagogic model does allow for more room for expression on the part of the students than those in either China or Indonesia. There is, however, as with the other two countries an inherent exam culture as students are prepared for competitive national exams that allow for continuation into Higher Education. A number of authors (Meyer and Boulton-Lewis, 1999; Meyer and Kiley, 1998; Phillips et al., 2002; Sadler-Smith, 1999; Wierstra et al., 2003; Wu, 2002) concur in the idea that the international student will base their approach to learning in part on prior educational experience. For this research it is of interest to see whether this translates into differences in learning styles preferences. Aims Overall the aim here is to examine the relationship between learning styles and culture. More specifically the objectives are to: (1) Compare, at the outset of the period of higher education and for students of selected cultural backgrounds: . their learning style preferences; and . their item by item responses to the learning style instrument used. (2) Discuss preliminary findings in light of the educators/educative practice. Methodology The research presented here is part of a larger project in which multiple samples of students in their first semester (n ¼ 315) and sixth semester (n ¼ 189) of Higher

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Education were compiled over a three-year period. The students, in the age range of 21-26, were all on the same programme and working towards a bachelors degree in hospitality management. The Institute at which the research was carried out, being private, is a high-fee charging institute and the students all belong to a high socio-economic group. The samples were of international students who were grouped into cultural clusters. Cross-sectional as well as longitudinal results were analysed (Charlesworth, 2005, 2007). Subsequent to this, a sub-sample of Indonesian, Chinese and French students (n ¼ 113) has now been further analysed. Although nationality was taken as the basis, all the students in the sub-sample have grown up and been schooled in their home culture. Students in the sub-sample were selected in order to make a comparison between students of Eastern and Western origins. The sample was, however, opportunistic in that these were the only cultures for which there were sufficient numbers. The instrument used was an adaptation of the Honey and Mumford Learning Styles Questionnaire (LSQ) to allow for the use of a six-point Likert scale in place of the original “agree” and “disagree” dimensions (Lashley, 2002). This in no way changes the instrument, however, it does allow for a more precise measure of learning styles preference. As work on the learning style preferences of hospitality management students had already been carried out in the UK and Australia (Barron and Arcodia, 2002; Lashley, 2002; Wong et al., 2000) using the same instrument, the decision was made to use it in this study largely for comparative purposes. The instrument itself is a self-scoring questionnaire consisting of 80 questions. Each question is related to a specific style with 20 questions per learning style, allowing for a maximum score of 100 points per style. For the purpose of this study a second section was added to the questionnaire in order to: identify the nationality/nationalities of the student; establish where their schooling had been accomplished; establish in which language their schooling had been accomplished. The questionnaire was administered during regularly scheduled class time; its completion was voluntary with the large majority of students filling it out. The results from two different analyses are presented. In the first, a comparison has been made using one-way ANOVA of the learning style preferences of the students in the three cultural groups making up the sub-sample. This was to see whether the differences between the cultural groups would be larger than the differences within the groups. The total score for each style was used as the basis for this. The second analysis takes a look, item by item, at the responses to the instrument used. This is done for each of the four styles separately. Results Analysis 1: Indonesian, Chinese and French sub-sample The data from these three groups was analysed by running a one-way ANOVA to ascertain if and to what extent there were statistically significant differences in learning style preferences between the 3 groups. Results were statistically significant, as shown in Table I, for three of the four learning styles: activist, reflector and pragmatist but not for theorist suggesting that further analysis might shed more light on a possible culture connection. Cohen’s f (Cohen, 1988), the average standardized difference between the three sample groups, or the effect size, has also been calculated. Cohen’s f is interpreted as: small effect size f ¼ 0.10; medium effect size f ¼ 0.25; large

effect size f ¼ 0.40. Thus the size of the differences between the three groups for activist, reflector and pragmatist styles could be classified as moderate. Figure 1 shows the learning style preference results for the three cultural subgroups in the first semester of study (S1). Indonesian students scored the lowest on the activist scale and highest on the reflector scale, the Chinese students the highest on the theorist scale and about the same as the Indonesians on the activist scale. The French sample shows a considerably higher score on the pragmatist scale which sets them apart from their peers. Similar patterns, however, were not found when considering students from the same subgroups in their sixth semester of study (S6) where no significant differences in style were found among the different cultural groups; this may reflect the relatively smaller size of the S6 and somewhat different cultural composition of this group compared to those in semester one. This may also reflect the impact of enculturation inculcated as a result of time spent within the current working environment. Differences between the S1 and S6 cohorts was confirmed by statistical analysis; highly significant differences

Learning style Activist Reflector Theorist Pragmatist

F-value

p

Cohen’s f

5.987 7.223 0.425 4.275

0.003 0.001 0.655 0.016

0.30 0.33 ,0.00 0.24

Notes: Indonesian n ¼ 34; Chinese n ¼ 41; French n ¼ 38

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Table I. S1 selected sample results

Figure 1. Learning style preferences

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were found between the S1 and S6 subgroups samples on the theorist and pragmatist styles but not on the activist and reflector styles. Despite these being cross-sectional results they mirror what has been shown in longitudinal research (Charlesworth, 2007).

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Discussion The S1 sample provides us with a picture of just how different the learning style preferences of culturally diverse students can be, at least at the outset of their studies. The high reflector, low activist first-year Asian student is consistent with much of the research that has already been done and also seems to be in agreement with Barron who suggests that: . . . it could be argued that a student commencing tertiary level study in a western university will tend to adopt the learning method with which they are most familiar. That is, those methods they have been exposed to and used in their primary and secondary education” (Barron, 2002, p. 32).

Despite a lack of consensus in the literature regarding the extent to which Asian students are rote versus deep learners (Kember and Gow, 1991), the data here suggest that the Asian students are highly reflector. Surprisingly both the Chinese and the French show up as relatively activist in their preferences. The French sample further distinguishes itself in its score on the pragmatist style. Analysis 2: Variations in style profiles among cultural groups The data already presented show differences in learning style preferences between the three subgroups on the basis of the total scores per learning style. The results have further been analysed on an item by item basis in order to shed light on where these differences lie using one-way ANOVA. The results are presented style by style with only those questions showing a statistically significant difference in the response between the sub-groups being exposed. Activist. As per the data presented in Table II and Figure 2, the activist learning style is the one that is rather less embraced by the Eastern students, especially the Indonesians. This is a style that calls for someone to be extroverted, enjoy challenge and having a propensity to rush right into things. It is also a style that would not fit too well with someone who likes to take their time, avoids risks and would not want to make a fool of themselves. The overall response pattern for this style shows the French students scoring the highest with the Chinese students in the middle and the Indonesians the lowest. Reflector. This is the second learning style which shows the most frequent and highest statistically significant differences between the sub-groups. Students who show a preference for this learning style tend to take their time about things, listen well to others and do not try to impose themselves (see Table III and Figure 3). Figure 3 shows to just what extent the Indonesian students fit the characteristics shown below with scores in the 4-5 range for things related to listening, thinking carefully and not liking deadlines. It also highlights to what extent the French students are less of this nature, especially with respect to rushing things and weighing in the opinions of others. The Chinese students, as with the activist style seem to place themselves on many of these questions between the Indonesian and French students. Theorist. The theorist learning style preference is of interest being the one that, for all of the students sampled (Charlesworth, 2005), shows an increase over the course of

Question no.

Learning style: Activist

10 17

I actively seek out new experiences I’m attracted more to novel, unusual ideas than to practical ones I thrive on the challenge of tackling something new and different Quiet, thoughtful people tend to make me feel uneasy In discussions I usually produce lots of spontaneous ideas More often than not, rules are there to be broken I’m usually one of the people who puts life into a party I enjoy the drama and excitement of a crisis situation

23 38 43 45 72 79

F-values

p

Cohen’s f

8.932

0.000

0.37

3.226

0.043

0.20

10.008 7.020

0.000 0.001

0.40 0.33

4.783 4.136

0.010 0.019

0.26 0.24

11.915 3.712

0.000 0.028

0.44 0.22

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Table II. Activist-related questions

Figure 2. Activist style: mean responses, on questions showing statistically significant differences, by cultural group

study. Here again we see that there are but few differences, even on the individual questions, between the cultural sub-group students (see Table IV and Figure 4). The characteristics of this learning style preference can be summed up in one word – organization. It is perhaps not so surprising then that almost all of the students increase their preference for this style as they proceed through Higher Education and as this becomes more necessary for success in their studies.

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Question no. 7 13 15

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25 31 36 39 41 62

Table III. Reflector-related questions

Figure 3. Reflector style: mean responses, on questions showing statistically significant differences, by cultural group

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Learning style: Reflector I like the sort of work where I have time for thorough preparation and implementation. I take pride in doing a thorough job I take care over the interpretation of data available to me and avoid jumping to conclusions I pay meticulous attention to detail before coming to a conclusion I listen to other people’s points of view before putting my own forward It worries me if I have to rush out a piece of work to meet a tight deadline I often get irritated by people who want to rush things I think that decisions based on a thorough analysis of all the information are sounder than those based on intuition In discussions I am more likely to adopt a “low profile” than to take the lead and do most of the talking It’s best to think carefully before taking action

F-values

p

Cohen’s f

3.412 13.601

0.037 0.000

0.21 0.47

4.719

0.011

0.26

4.366

0.015

0.24

6.217

0.003

0.30

14.231

0.000

0.48

4.041

0.020

0.23

4.652

0.011

0.25

10.395 6.389

0.000 0.002

0.41 0.31

Pragmatist. The last style that is being measured is that of the pragmatist learner, the student who takes things apart and puts them back together again, more often than not, their way. This style shows a less stable pattern than any of the others in the data collected. Perhaps it is actually the one that takes the longest for a student to cultivate and to settle on for themselves (see Table V and Figure 5). There are a number of differences between the students sampled worth highlighting here. The French students come across as direct, practical, lets-get-on-with-it, whatever-it-takes type learners in contract to the Indonesians who do not seem to be interested in finding new ways of getting things done and who above all will not hurt or upset anyone along the way.

Question no. 1 14 47

Learning style: Theorist I have strong beliefs about what is right and wrong, good and bad I am keen on self-discipline such as watching my diet, taking regular exercise, sticking to a fixed routine etc. . . I can often see inconsistencies in other people’s arguments

F-values

p

Cohen’s f

4.029

0.020

0.23

3.849

0.024

0.22

8.523

0.020

0.36

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Table IV. Theorist-related questions

Figure 4. Theorist style: mean responses, on questions showing statistically significant differences, by cultural group

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Question no. 5 35

124

44 54 65

Table V. Pragmatist-related questions

70

Learning style: Pragmatist I have a reputation for saying what I think simply and directly I tend to be attracted to techniques such as network analysis, flow charts, branching programmes, contingency planning etc. . . In discussions I put forward practical realistic ideas. In discussions I get impatient with irrelevances and digressions. I tend to reject wild spontaneous ideas as being impractical I don’t mind hurting people’s feelings so long as the job is done

F-values

p

Cohen’s f

11.335

0.000

0.43

3.437 3.774

0.036 0.026

0.21 0.22

4.938

0.009

0.26

4.566

0.012

0.25

10.077

0.000

0.40

Figure 5. Pragmatist style: mean responses, on questions showing statistically significant differences, by cultural group

Discussion Based on the S1 data there is definitely an East West divide at the outset of the period of higher education; with the Eastern students showing a higher preference for the reflector style and a lower preference for the activist style than their Western colleagues. Table VI pulls together the profile building exercise begun in the previous section. Looking at the questions which differentiate our populations we can see that two aspects, which seem to be related to time requirements and to respect, stand out. For

Indonesian

Chinese

French

Tends to prefer the tried and tested practical ideas At ease around quiet and thoughtful people Takes time to think things through very carefully, paying attention to others Does not like to be rushed or have tight deadlines Tends to reject new or wild ideas A lot of respect for others including their feelings

Likes challenge to a certain extent, will seek out new experiences Seems less comfortable with rules Willing to take time to listen and puts stock by analysis Interested in new ideas and ways of doing things Respects people including their feelings

Enjoys the challenge of something new and different which they will seek out More-or-less agrees to take time to listen but is not bothered by having to rush or work to a tight schedule Does not waste time Puts forth own interpretation if not convinced by others Seems to be less self-disciplined

the Indonesian students it is of extreme importance that they have time to plan, implement and carry things out. There also seems to be certain deference to authority, or to teachers operating here. The question of time also seems to be something of pertinence. Although both respect and time seem to place a role in how the Chinese students go about their learning it seems to be of lesser importance than for their Indonesian colleagues. For the French group neither of these aspects seem to carry a lot of weight. Conclusion In conclusion, the question of implications for educators remains to be addressed. It seems safe to say that today’s world of Higher Education is highly competitive and that is has seen a marked rise in the numbers of students studying outside of their home country. Educators who have an understanding of the differences, whether they be in the actual learning styles or in the approach to learning (Meyer and Kiley, 1998), that the students might have will be better equipped to welcome these students through the preparation of introductory programmes that are suited to an international clientele. In relation to HE programme development and delivery for international cohorts it is important to note that the changes shown in style are occurring early on in the programme. This emphasizes the need to facilitate both the individual’s understanding of their own style preferences (Sadler-Smith and Smith, 2004) as well as of their new instructional environment and along with that, perhaps new expectations. Programmes that go past the traditional studying for exams and time management modules to seminars that look at learning in its entirety. Seminars that provide students with the skills necessary for success in higher education as well as providing them with a guideline concerning the types of behaviour, from class involvement to evidence of critical thinking, that will be expected from them. In the business sector practitioners involved in on-the-job and management training need also to take culture and its influences on one’s practice seriously. It is suggested that on-the-job training, particularly appreciated by the activist, learners be supplemented with handouts and theory for the more reflective and theorist learners. Alternatively, leadership and training courses at a more senior level, and possibly having a more theoretical base, need to be broadened with case studies or actual issues in order to address those individuals with more activist styles.

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References Barron, P. (2002), “Issues surrounding Asian students’ hospitality management in Australia: a literature review regarding the paradox of the Asian learner”, Journal of Teaching in Travel and Tourism, Vol. 2 Nos 3/4, pp. 23-45. Barron, P. and Arcodia, C. (2002), “Linking learning style preferences and ethnicity: international students studying hospitality and tourism management in Australia”, Journal of Hospitality, Leisure, Sport & Tourism Education, Vol. 1 No. 2, pp. 15-27. Charlesworth, Z.M. (2005), “Convergent learning styles in international higher education”, paper presented at the European Learning Styles Information Network (ELSIN), 10th Annual Conference, School of Management, University of Surrey, 13-15 June 2005. Charlesworth, Z.M. (2007), “Educating international hospitality students and managers: the role of culture”, International Journal of Contemporary Hospitality Management, Vol. 19 No. 2, pp. 133-45. Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences, 2nd ed., Lawrence Erlbaum, Hillsdale, NJ. Corwin, S.J. (2001), “Classroom communication in a very foreign land”, Theory into Practice, Vol. XVII No. 5, pp. 416-22. Crahay, M. (1996), L’art et la science de l’enseignement, Editions Labor, Bruxelles. Crahay, M. (1999), Psychologie de l’e´ducation, Presses Universitaires de France, Paris. Dooley, K. (2001), “Re-envisioning teacher preparation: lessons from China”, Journal of Education for Teaching, Vol. 27 No. 3, pp. 241-51. Fowler, F.C. and Poetter, T.S. (2004), “Framing French success in elementary mathematics: policy, curriculum and pedagogy”, Curriculum Inquiry, Vol. 34 No. 3, pp. 283-314. Honey, P. and Mumford, A. (1986), The Manual of Learning Styles, revised version, Peter Honey, Maidenhead. Kember, D. and Gow, L. (1991), “A challenge to the anecdotal stereotype of the Asian student”, Studies in Higher Education, Vol. 16 No. 2, pp. 117-28. Lashley, C. (2002), “Learning styles and hospitality management education”, The Hospitality Review, April, pp. 56-60. Lattuca, L. (2002), “Learning interdisciplinarity: sociocultural perspectives on academic work”, The Journal of Higher Education, Vol. 73 No. 6, pp. 711-39. Lave, J. (1997), “The culture of acquisition and the practice of understanding”, in Kirshner, D. and Whitson, J. (Eds), Situated Cognition: Social, Semiotic, and Psychological Perspectives, Erlbaum, Mahlwah, NJ, pp. 17-35. Lave, J. and Wenger, E. (1991), Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, New York, NY. Lelie`vre, C. (2000), “The French model of the educator state”, Journal of Education Policy, Vol. 15 No. 1, pp. 5-10. Lewis, R.B. (1997), “Learning styles in transition: a study of Indonesian students”, paper presented at the Annual Meeeting of the Japan Association of Language Teachers, Hamamatsu, Japan. Meyer, J.H.F. and Boulton-Lewis, G.M. (1999), “On the operationalisation of conceptions of learning in higher education and their association with students’ knowledge and experiences of their learning”, Higher Education Research and Development, Vol. 18 No. 3, pp. 289-302.

Meyer, J.H.F. and Kiley, G. (1998), “An exploration of Indonesian postgraduate students’ conceptions of learning”, Journal of Further and Higher Education, Vol. 22 No. 3, pp. 287-98. Newman, M., Trenchs Parera, M. and Pujol, M. (2003), “Core academic literacy principles versus culture specific practices: a multi-case study of academic achievement”, English for Specific Purposes, Vol. 22, pp. 45-71. Ngwainmbi, E.K. (2004), “Communication in the Chinese classroom”, Education, Vol. 125 No. 1, pp. 63-76. Novera, I.A. (2004), “Indonesian postgraduate students studying in Australia: an examination of their academic, social and cultural experiences”, International Education Journal, Vol. 5 No. 4. Phillips, W., Lo, S. and Yu, T. (2002), “Teaching techniques among Chinese international students in Christian colleges and universities”, Christian Higher Education, Vol. 1 No. 4, pp. 347-69. Reuchlin, M. (1991), Les diffe´rences individuelles a` l’e´cole, Presses Universitaires de France, Paris. Rogoff, B. (1990), Apprenticeship in Thinking, Oxford University Press, New York, NY. Sadler-Smith, E. (1999), “Intuition-analysis style and approaches to studying”, Educational Studies, Vol. 25 No. 2, pp. 159-73. Sadler-Smith, E. and Smith, P. (2004), “Strategies for accommodating individuals’ styles and preferences in flexible learning programmes”, British Journal of Educational Technology, Vol. 34 No. 4, pp. 395-412. Trommsdorf, G. and Dasen, P.R. (2001), “Cross-cultural study of education”, in Smelser, N. and Bates, P. (Eds), International Encyclopedia of the Social and Behavioural Sciences, Elsevier, Amsterdam, pp. 3003-7. van Zanten, A. (2002), “Educational change and new cleavages between head teachers, teachers and parents: global and local perspectives on the French case”, Journal of Education Policy, Vol. 17 No. 3, pp. 289-304. Wierstra, R.F.A., Kanselaar, G., Van de Linden, J.L., Lodewijks, H.G. and Vermunt, J.D. (2003), “The impact of the university context on European students’ learning approaches and learning environment preferences”, Higher Education, Vol. 45 No. 4, pp. 503-23. Wong, K.K.F., Pine, R.J. and Tsang, N. (2000), “Learning style preferences and implications for training programs in the hospitality and tourism industry”, Journal of Hospitality and Tourism Education, Vol. 12 No. 2, pp. 32-40. Wu, S. (2002), “Filling the pot or lighting the fire? Cultural variations in conceptions of pedagogy”, Teaching in Higher Education, Vol. 7 No. 4, pp. 387-95. About the author Zarina M. Charlesworth is a lecturer in Marketing, Services and Knowledge Management at the Glion Institute of Higher Education in Switzerland. Building on her experience and her educational background, with a BA in anthropology from Cornell University, an MBA from Murdoch University, Western Australia, a Masters in Advanced Studies in Education and a PhD in Educational Sciences from the University of Geneva, Switzerland, her interest is now focussed on the management of international higher education, including programme development and course delivery. She can be contacted at: [email protected] To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints

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Emma Kingston Abstract Purpose – The purpose of this paper is to compare the emotional competence of first year undergraduates enrolled on a high or low drop-out rate (HDR and LDR, respectively) course, at a newly established university within the UK. Design/methodology/approach – A mixed methods approach using both quantitative and qualitative data collection methods was used. The Trait Emotional Intelligence Questionnaire (TEIQue) established participants’ emotional competence, and semi-structured interviews were used to probe the findings from the TEIQue. Findings – The results indicate that typical HDR course participants have high self-esteem and a good level of interpersonal skills, but are controlled by their emotions and exhibit an external locus of control. This manifests itself in a distrust of peers as a source of support and a reactive attitude to self-improvement. Typical LDR course participants have low self-esteem and a good level of intrapersonal skills, but have developed the ability to control their emotions and exhibit an internal locus of control. This manifests itself in a high level of confidence in peers as a source of support and a proactive attitude to self-improvement. Originality/value – The paper contributes to the learning styles literature by investigating the impact of students’ characteristic affective behaviours on their vulnerability to drop-out. Keywords Emotional intelligence, Competences Paper type Research paper

Introduction Emotions have the power to render intellectual endeavour as futile, which can result in withdrawal from a task (or indeed course), or at the other extreme, to motivate and enthuse learners to progress (Ellison, 2001). In essence, “affect surrounds cognition” (Alsop and Watts, 2003, p. 1046). This view is corroborated by Oatley and Jenkins (2002, pp. 122-3) who refer to emotions as the guidelines for our lives, “. . .the very centre of human mental life. . . [and] complement the deficiencies of thinking”. As such, investigation of the characteristic affective behaviours of students (Keefe, 1979) may equip us with a greater understanding of the learning process and the areas where student vulnerability may occur. This paper will outline some issues involved in student drop-out from HE, followed by a brief overview of the affective side to learning.

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Student drop-out UK universities have a history of comparatively low drop-out rates. This has previously been credited to a variety of reasons, including, the “highly selective entry system”, “well-developed tutorial framework” (Rickinson and Rutherford, 1995) and small student-staff ratios (Ozga and Sukhnandan, 1998). However, current government policies has caused rapid growth in student numbers and subsequently transformed the funding system, creating widespread disquiet regarding standards and drop-out rates. The UK Government has treated the issue with increasing commitment, by

asking the central authorities to “bear down” on student drop-out (Parry, 2002). Indeed, Margaret Hodges (2003, in Cartney and Rouse, 2006) argued that drop-out rates were as high as 33 per cent and 45 per cent in some universities and that the increasingly diverse student population had the potential to hinder or even prevent integration, particularly in the first year of study (Brunsden et al., 2000). It therefore appears that there is a trade-off between the diversity of the student body and drop-out rates. An inherent part of the academic system is the competition integral to degree classification that will always breed system losers. However, academic failure is not a reliable indication of whether a student will persist with, or discontinue their studies (Peelo and Wareham, 2002). In reality, academic failure is argued to account for less than one-fifth of discontinuing students. Indeed, Codjoe and Helms (2005) have described positive, neutral and negative student drop-out. Positive student drop-out can occur when a student transfers to another institution better suited to their circumstances, or obtains the skills they require and leaves before qualifying. Neutral student drop-out can occur when a student has priorities external to the university and must leave to deal with them. Finally, negative student drop-out, occurs when a student is failing academically or simply not ready to cope with the trials and tribulations of HE. The variety of ways in which student drop-out data can be categorised, may lay responsibility at different doors. For instance, the Higher Education Statistics Agency (HESA) have been criticised for using data that blames the students for student drop-out, ignoring that some failings may lie with the institution (Reimann, 2004). This has the potential to seriously effect a student’s self-efficacy, and result in the “. . . risk of blaming the victims of circumstances, not of their doing and of institutions failing to submit themselves to a level of self-scrutiny” (Yorke, 1999, p. 10). On the other hand, high levels of student drop-out will always reflect poorly on an institution, regardless of the student body (Ashby, 2004). To place blame for student drop-out with one source is entirely illogical, due to the interactive nature of teaching and learning in HE. Ozga and Sukhnandan (1998, p. 319) argue that, “. . . we need to understand non-completion as a process of student-institution negotiation”. As such, it is not surprising that the reasons for non-failing students to discontinue their studies are still unclear in two-fifths of cases (McLausland et al., 2005). In addition, academic and intellectual factors have been claimed to account for 25 per cent of the variance in academic performance (Larose et al., 1998; Goleman, 1996; Mouw and Khanna, 1993, in Larose et al., 1998), leaving 75 per cent of variance attributable elsewhere. Exit interviews asking students for a single reason as to why they are leaving, does not do justice to the multitude of possible circumstances that may have led to the decision to drop-out. The work on emotional intelligence has highlighted how positive affect can encourage more flexible and multifaceted memory, whereas negative affect can illicit the narrowing of memory, or even the prevention of effective focus on work. The impact of students’ emotional competence in coping with the decision to drop-out, or persist, therefore, presents a potentially highly influential factor that requires further investigation. Affective domain Positive attitude is argued to be the basis of an inflation of energy expenditure, determination, and commitment to a task (Schreiber, 2002). Indeed, Blunsden et al.

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(2003) postulate that subject enjoyment can lead students into believing that they will not only be able to apply their learning to specific situations, but also that they have learned more. This view represents a growing body of research that rejects the traditional perspective of emotional irrationality as an encumbrance on intellectual endeavour, and embraces the idea that even the minutest emotion can have a positive and rationalising impact on the facilitation of learning (Isen, 1993). The influence of individual differences on academic performance has become increasingly popular, for which intervention with at risk students has been paramount (Larose et al., 1998). The popularisation of emotional intelligence (EI) (Goleman, 1996) has been accompanied by a growing body of research, which concludes that, in a highly intelligent academic environment, it is the emotional factors rather than intellectual ability that have a higher predictive validity of success (Cherniss, 2000). Indeed, Goleman (1998, p. 313) offers an insightful theory of EI that extols the virtues of utilising an emotionally intelligent approach to education, “emotional intelligence is now as crucial to our children’s futures as the standard academic fare”. However, this influx of largely unstructured investigations has resulted in somewhat contradictory and confusing findings, which has produced considerable debate in the race to develop a measurement tool that effectively captures the EI constructs (Emmerling and Goleman, 2003). In addition to this, Furnham and McManus (2004) point out, while formal research investigates the views of the academic staff, political and external influences as a matter of course, the views of those most likely to feel the effects of such research are all too often neglected. In other words, it is the students experiencing the emotional reactions who are the essential human ingredient in the formula (Goleman, 1998). As such, successful academic learning is argued to be maintained through effective emotional development (Greenhalgh, 1994). Ability and trait EI were suggested to represent two distinct constructs, which could be distinguished by the measurement method utilised to operationalise them, as opposed to their sampling domains (Petrides and Furnham, 2000). Ability EI represents actual ability to recognise, manage, and employ emotion-laden information, whereas trait EI represents a group of behavioural characteristics and self-awareness regarding recognising, managing, and employing emotion-laden information. Ability EI is measured via maximum-performance tests and refers principally to cognitive ability, for example the Mayer-Salovey-Caruso (Mayer et al., 2002) Emotional Intelligence Test (MSCEIT). Trait EI on the other hand, is measured via self-report questionnaires and refers principally to personality theory, for example Petrides’ (2001-2006) Trait Emotional Intelligence Questionnaire (TEIQue), which shall be discussed below. As the present study aims to investigate the impact of student emotional competence and drop-out rates, it is trait EI that falls within the focus of this paper. Methodology Participants Participants were selected due to criterion of relevance to the research question. Once all available data regarding student completion rates had been examined, spanning 2000 to 2005, two programmes were selected that either exhibited consistently high

student drop-out rates (referred to herewith as the HDR Course), or consistently low student drop-out rates (the LDR Course). On studying the HDR Course data, an average of 27 per cent (taken from the University’s Planning Office web site) of its students’ were failing to complete the full programme. This represents a disproportionately large number of students in comparison to the university’s overall completion rates, cited as 16.1 per cent (Curtis and Hall, 2005). The LDR Course, on the other hand, had an average of 17 per cent of its students’ failing to complete the full programme, which represents a similar number of students failing to graduate in comparison to the university’s overall completion rates, and the national average, cited as 15.6 per cent in 2004/2005 (HESA, 2006). The course convenor for each of these subjects was then approached and student samples were made available through these contacts. Of the 103 students involved, 52 were from the HDR Course, and 51 were from the LDR Course. Within this sample, the HDR Course contained 39 per cent men and 61 per cent women (35 per cent mature (over 21) and 65 per cent younger students), and 36 per cent of the sample labelled themselves as “White – UK”. The LDR Course on the other hand, contained 12 per cent men and 88 per cent women (57 per cent were mature and 43 per cent were younger students), and 60 per cent of the sample labelled themselves as “White – UK”. Therefore, the HDR Course had a larger proportion of men, a younger cohort of participants, and a more diverse ethnic spread compared to the LDR Course. Whereas the LDR Course had a larger proportion of women, a more mature cohort of participants, and a less diverse ethnic spread. Interviews Of the participants, 10 per cent were randomly selected to be involved in the qualitative aspect of the research (five participants from each course), enabling the issues emerging from the quantitative data to be discussed in greater depth. Research tools Quantitative: Trait Emotional Intelligence Questionnaire (TEIQue). Petrides and Furnham (2000) were able to identify trait EI as “. . . a constellation of emotion-related dispositions and self-perceived abilities representing a distinct composite construct at the lower levels of hierarchical personality structures” (Petrides et al., 2004). This should result in behavioural responses that remain consistent across different contexts (Petrides and Furnham, 2000) and thus represent relatively established characteristic affective behaviours (Keefe, 1979). Accordingly, Petrides (2001-2006) developed the Trait Emotional Intelligence Questionnaire (TEIQue), which revealed that trait EI has incremental validity over the main personality components, thus dispelling the theoretical inadequacies of previous EI research. Petrides et al. (2004) argue that the TEIQue is the only measurement tool that encompasses the entire sampling domain for trait EI. The full version of the TEIQue (v.1.50) consists of 15 facets or subscales, which are incorporated into four wider factors, consisting of “emotionality”, “self-control”, “sociability”, and “well-being”, and a global trait EI score. The TEIQue, therefore, aims to assess a person’s subjective perspective of their temperament, rather than objective cognitive abilities such as IQ. Petrides et al. (2004) argue that trait EI explains a person’s current abilities rather than

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predicting what they may achieve in the future, which is advocated by Dweck (1999) who labels the idea of assessing a person’s present skills to predict future success as a “fallacy”. In addition to this, Petrides et al. (2004) have pointed to vulnerable or at risk students as particularly susceptible to trait EI, because they are more likely to suffer emotional struggles and increased pressure during the course of their studies. To date, the TEIQue has been shown to have good predictive validity for a range of areas including job performance and deviant behaviour in school (Petrides and Furnham, 2001; Petrides et al., 2004). As such, it would seem a logical step to extend the realm of trait EI testing into higher education, as the present study aims to do. Semi-structured interview A semi-structured interview schedule was developed to gain a greater insight into the reasons behind participants’ TEIQue responses, as well as allowing participants to direct the line of questioning and/or clarify any questions they did not fully understand. The interview schedule focused on three areas; questions related to participants’ experience of HE and learning in general, participants’ emotional reactions to university, and participants’ reactions to emotionally-laden information, such as feedback. The interview schedule included: . Descriptive questions, used to elicit more general descriptions of various situations. . Structural questions, used to elicit how the participant categorises their knowledge of the world. . Contrast questions, used to elicit comparative responses to a given situation. . Evaluative questions, used to elicit the participants feelings about a given experience (Willig, 2001). Results Quantitative: TEIQue Standard multiple regression was performed between the combined participant samples for global Trait EI (dependent variable) and the four factors: (1) well-being; (2) self-control; (3) emotionality; and (4) sociability. The regression was a very good fit, and the overall relationship was highly significant (F6, 94 ¼ 2333.35, p , 0.0005), indicating that the four factors described Trait EI well. In addition, a further regression was performed between the combined participant samples for global Trait EI and the three extraneous variables: (1) gender; (2) age; and (3) ethnicity. The regression indicated that they did not significantly influence the global Trait EI scores.

An independent samples t-test was conducted to compare the TEIQue scores of the HDR and LDR Course participants. In total, 20 comparisons were made: . the Global Trait EI score; . Four Trait EI Factors; and . 15 Trait EI Facets (Petrides, 2001-2006). Table I highlights the scores found to be statistically significantly different between the courses. There were no statistically significant differences between the scores of the HDR and LDR Course participants for: . Global Trait EI; . Two Trait EI Factors (Emotionality, and Self-control); and . A total of 11 Trait EI Facets (Adaptability, Emotion Perception, Emotion Expression, Emotion Management, Emotion Regulation, Impulsiveness, Relationship skills, Self-motivation, Stress management, Trait happiness, or Trait optimism).

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In light of these results it would appear that participants’ general behavioural characteristics and self-awareness regarding recognising, managing, and employing emotion-laden information were reasonably similar. However, there are several areas in which the lower level personality traits of the participants do differ significantly. With regard to the four Trait EI factors, the HDR Course participants produced significantly higher scores on the “Sociability” and the “Well-being” factors than their LDR Course peers. This indicates that the HDR Course participants perceive themselves to be more extroverted and confident of their interpersonal skills, as well as being generally more satisfied with life, and having higher self-esteem. Thus suggesting that LDR Course participants perceive themselves to be more introverted and less confident of their intrapersonal skills, as well as being generally less satisfied with life, and having diminishing levels of self-esteem. On first impression, these results suggest that the LDR Course participants have more negative traits than their HDR Course peers, which could be considered a somewhat surprising finding. However, this result may indicate that low self-esteem is itself, not a sole indicator of drop-out, but may instead point to the multifaceted nature of factors that interact to affect the decision to drop-out or persist with university.

TEIQue Trait EI factors Sociability Wellbeing Trait EI facets Self-esteem Trait empathy Social competence Assertiveness

HDR course M SD

LDR course M SD

t-test t(103)

p

4.75 5.05

0.55 0.73

4.36 4.68

0.66 0.91

3.14 2.23

0.002 0.03

4.81 4.74 4.79 4.82

0.72 0.68 0.67 0.68

4.19 5.04 4.48 4.24

1.07 0.69 0.88 0.92

3.40 2 2.14 2.01 3.53

0.001 0.034 0.047 0.001

Table I. Significant t-test results between HDR and LDR course participants

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Additionally, during his extensive research on self-esteem, Emler (2001) noted that low self-esteem did not result in reduced academic performance, but that high self-esteem could result in unrealistic expectations and the rejection of any information that had the potential to contradict this. These results, therefore, highlight the typical HDR Course participant as confident in their abilities and outspoken, with a good level of interpersonal skills, whereas the typical LDR Course participant is empathetic and reflective, with a good level of intrapersonal skills. On consideration, these results may not be as surprising as first indicated. The HDR Course participants’ high self-esteem may be influential in artificially inflating their other scores. Additionally, while good interpersonal skills are no doubt an asset for social integration into university, the intrapersonal and reflective skills exhibited by the LDR Course participants, may well be more beneficial to academic integration into university. As academic integration is rewarded by the university, in the form of better grades due to a more comprehensive understanding of the implicit academic rules that guide the marking criteria, this perhaps reduces a student’s vulnerability to dropping out. Emerging themes Interpretative Phenomenological Analysis (IPA) was used to analyse the qualitative data emerging from the semi-structured interview. This revealed four master themes: (1) emotional reaction; (2) adaptation; (3) student support; and (4) Influences on learning. Table II summarises the key findings for each master theme, for the HDR and LDR course participants. Master theme

Table II. Key issues discussed within the master themes for the HDR and LDR course participants

HDR course participants

Emotional reaction Greater abundance of positive affect Focus on emotional influences on learning Adaptation Negative situations ignored and accepted Reactive attitude to self-improvement Lack of preparation for H Student support Feedback seen as beneficial but sometimes inadequate Academic staff: main source of support Peers: not seen as valid source of support Criticised inconsistency between staff Influences on High levels of self-esteem learning External loci of control Extrinsic motivation Notes: n ¼ 5; n ¼ 10

LDR course participants Greater abundance of negative affect Focus on emotional control for learning Negative situations addressed and adapted to Proactive attitude to self-improvement Poor preparation for HE Feedback seen as beneficial but often inadequate Academic staff: good source of support Peers: seen as a good source of support Criticised inconsistency in course as whole High levels of self-doubt Internal loci of control Intrinsic motivation

As may be seen in Table II, there are 12 key themes that have arisen from the participants’ accounts, each of which has been placed within its respective master theme. These findings characterise the HDR Course participants as perceiving themselves to be controlled by their emotions and exhibiting an external locus of control, allowing them to maintain their high levels of self-esteem and the positive affect with which they approach their education. While they cite academic staff and feedback as highly beneficial sources of support, they fail to trust their peers as additional sources of academic support, and will only seek personal development on instruction to do so. Their lack of preparation for HE means that these students often feel lost in their first year, which can produce a spiral of negative affect and, if ignored, has the potential to increase the student’s vulnerability to dropping out. The findings from the LDR course participants, on the other hand, characterise them as perceiving themselves to be more in control of their emotions, having developed new skills in reading and coping with emotional reactions. These students exhibit an internal locus of control, which results in high levels of self-doubt and negative affect, but which also serves to drive their intrinsic motivation to continually and proactively develop. While they cite academic staff and feedback as beneficial sources of support, they also trust their peers as an additional source of support where appropriate. The LDR course participant’s poor preparation for HE thus fuels their motivation to proactively adapt to the new environment and, therefore, decreases their vulnerability to dropping out.

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Conclusion It would appear that there are significant differences in the emotional competencies, as well as more generally, among the participants attracted to the High and Low Courses, as may be seen in Table III.

TEIQue results HDR course participants Greater abundance of positive affect High level of self-esteem More content with life Greater belief in likelihood of success Extroverted in social situations More forthright and likely to stand up for their rights LDR course participants Greater abundance of negative affect Low levels of self-esteem More dissatisfied with life Introverted in social situations More empathetic Feel that they are less able to influence others’ emotions

IPA results Greater abundance of positive affect Focus on emotional influences on learning High level of self-esteem Reactive attitude to self-improvement Peers: not seen as valid source of support Extrinsically motivated External locus of control Greater abundance of negative affect Focus on emotional control for learning High levels of self-doubt Proactive attitude to self-improvement Peers: seen as a good source of support Intrinsically motivated Internal locus of control

Table III. Summary of differences in quantitative (TEIQue) and qualitative (IPA) results

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In light of Table III, it is important to first note that Alsop and Watts’ (2003, p. 1046) contention that “affect surrounds cognition”, does seem to be reflected in the accounts of both High and Low Course participants, for which direct descriptions of emotional reaction as well as the indirect impact that emotional reactions can have, are frequently referred to. The TEIQue and interview data highlight a division between participants however, in which both sets of results indicate that High Drop-Out Rate Course participants exhibit an abundance of positive affect, whereas the Low Drop-Out Rate Course participants exhibit an abundance of negative affect. This would seem to add weight to the conclusion of Blunsden et al. (2003), who argue that positive affect increases a student’s belief that they have learned more and, therefore, are more able to apply their knowledge. In other words, perhaps the abundance of positive affect in the HDR Course participants helps to maintain their high levels of self-esteem, which could result in diminishing levels of motivation for self-improvement; why would one seek to improve, if one is satisfied with one’s progress? It is probable that, rather than narrowing memory capacity and preventing focus on a task, the negative affect exhibited by the LDR Course participants is linked with their dissatisfaction with life and self-doubt, thus fuelling participants’ proactive attitude to self-improvement. Therefore, perhaps a reason for the differential drop-out rates may lie in master theme one (emotional reaction). While HDR course student accounts focused on describing how emotions influenced their learning, LDR course student accounts focused on describing how participants had developed new skills to control the effect of emotions on their learning. In light of this, Greenhalgh (1994) would argue that LDR Course participants are far more likely to be successful at academic learning, due to the emotional development that their accounts reflect, as opposed to the HDR Course participants, who appear to be controlled by their emotions. In an extension of Goleman’s (1998) theory of EI, perhaps being aware of the influences of emotions, as are the HDR Course participants, provides a foundation for the emergence of academic self-esteem, whereas developing skills to control these emotions, as have the LDR Course participants, provides a foundation for the emergence of autonomous learning. Therefore, if the LDR Course participants do not enter university with the academic attributes extolled by HE, such as autonomous learning, it would appear that they have the ability to develop these attributes more effectively than their HDR Course peers, thus reducing their vulnerability to dropping out. This conclusion may be further exaggerated by participants’ views of their peers. While the HDR Course participants believe themselves to be good at communicating with their peers, they only tend to do so in social, rather than academic, situations. Therefore, this could fuel the sense of isolation they describe as being particularly poignant in their first year, which interestingly, has been repeatedly shown to be the year in which the majority of drop-out occurs (Brunsden et al., 2000). Finally, not only should student drop-out be considered as a process of student-institution negotiation (Ozga and Sukhnandan, 1998), but the present study’s results also point to the importance of participants’ beliefs regarding the level of control they have over their learning. The HDR Course participants’ extrinsic motivation and external loci of control, reflect these participants view that their academic success is seated within sources external to them, and therefore out of their control. In light of this, the HDR Course participants may perceive drop-out as something that happens to them that they must accept. The LDR Course participant’s

intrinsic motivation and internal loci of control, on the other hand, reflect these participants view that their academic success is seated within their internal resources, and is, therefore, something within their control. As such, the LDR Course participants may perceive drop-out as something that they are capable of fighting against and can be avoided.

Emotional competence

Implications for practice In response to the initial question, “What influence does a student’s emotional competence have on drop-out rates from undergraduate courses?” the present study’s results would seem to support the view of researchers such as Goleman (1996), Petrides and Furnham (2001), and Cherniss (2000). These theorists highlight the importance of emotional factors in academic success, and thus failure. A student’s level of emotional competence could, therefore, be argued to influence a significant part of the 75 per cent of variance in academic performance, which cannot be attributed to academic and intellectual factors (Larose et al., 1998; Mouw and Khanna, 1993, in Larose et al., 1998). As such, a student’s emotional competence is likely to play a key role in the decision to drop-out from university. Higher Education should, therefore, be an experience that promotes self-awareness, peer support and a proactive attitude to self-improvement. Finally, the affective behaviours of students would seem to, not only “serve as a relatively stable indicator of how learners perceive, interact with, and respond to the learning environment” (Keefe, 1979), but could also serve as an indication of how vulnerable students are to dropping out.

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References Alsop, S. and Watts, M. (2003), “Science education and affect”, International Journal of Science Education, Vol. 25 No. 9, pp. 1043-7. Ashby, A. (2004), “Monitoring student retention in the Open University: definition, measurement, interpretation and action”, Open Learning, Vol. 19 No. 1, pp. 65-77. Blunsden, B., Reed, K., McNeil, N. and McCachem, S. (2003), “Experiential learning in school science theory: an investigation of the relationship between student enjoyment and learning”, Higher Education Research and Development, Vol. 22 No. 1, pp. 43-56. Brunsden, V., Davies, M., Shevlin, M. and Bracken, M. (2000), “Why do HE students drop-out? A test of Tinto’s model”, Journal of Further and Higher Education, Vol. 24 No. 3, pp. 301-10. Cartney, O. and Rouse, A. (2006), “The emotional impact of learning in small groups: highlighting the impact on student progression and retention”, Teaching in Higher Education, Vol. 11 No. 1, pp. 79-91. Cherniss, C. (2000), “Emotional intelligence: what it is and why it matters”, paper presented at the Annual Meeting of the Society for Industrial and Organisational Psychology, available at: www.eiconsortium.org/research/what_is_emotional_intelligence.pdf (accessed 26 November 2004). Codjoe, H.M. and Helms, M. (2005), “A retention assessment process: utilizing total quality management principles and focus groups”, Planning for Higher Education, Vol. 33 No. 3, pp. 31-42. Curtis, A. and Hall, J. (2005), “Examining student drop-out at King’s College, University of London”, paper presented at Workshop, University Educational Development Centre, University of London, London.

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Dweck, C.S. (1999), Self-Theories: Their Role in Motivation, Personality, and Development, Psychology Press, Hove. Ellison, L. (2001), The Personal Intelligences: Promoting Social and Emotional Learning, Corwin Press, London. Emler, N. (2001), “Self-esteem: the costs and causes of low self-worth”, in Anderson, J.E. and Asbury, E.T. (Eds), Self-Esteem: The Myth of the Century, available online at: www. campbell.edu/faculty/asbury/sample_paper.pdf (accessed 20 July 2007). Emmerling, R.J. and Goleman, D. (2003), “Emotional intelligence: issues and common misunderstandings”, Issues in Emotional Intelligence, Vol. 1 No. 1, available at: www. eiconsortium.org/research/EI_Issues_And_Common_Misunderstandings.pdf (accessed 26 November 2004). Furnham, A. and McManus, I.C. (2004), “Student attitudes to university education”, Higher Education Review, Vol. 36 No. 2, pp. 29-38. Goleman, D. (1996), Emotional Intelligence: Why it Can Matter More than IQ, Bloomsbury Publishing Plc, London. Goleman, D. (1998), Working with Emotional Intelligence, Bloomsbury Publishing Plc, London. Greenhalgh, P. (1994), Emotional Growth and Learning, Routledge, London and New York, NY. HESA (2006), Performance Indicators in Higher Education in the UK, 2004/2005, available at: www.hesa.ac.uk/pi/0405/summary.htm (accessed 20 July 2006). Hodges, M. (2003), The Future of Higher Education: Fifth Report of Session 2002-2003, House of Commons Education and Skills Committee, London. Isen, A.M. (1993), “Positive affect and decision making”, in Lewis, M. and Haviland, J.M. (Eds), Handbook of Emotions, The Guilford Press, New York, NY and London. Keefe, J.W. (1979), “Learning style: an overview”, in Keefe, J.W. (Ed.), Student Learning Styles: Diagnosing and Prescribing Programs, National Association of Secondary School Principals, Reston, VA. Larose, S., Robertson, D.V., Roy, R. and Legault, F. (1998), “Non-intellectual learning factors as determinants for success in college”, Research in Higher Education, Vol. 39 No. 3, pp. 275-97. McLausland, W.D., Mauromaras, K. and Theodossiou, I. (2005), “Explaining student retention: the case of the University of Aberdeen”, Widening Participation and Lifelong Learning, Vol. 7 No. 3, pp. 24-6. Mayer, J.D., Salovey, P. and Caruso, D.R. (2002), “The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT): user’s manual, multi-health systems, Toronto”, in Schulze, R. and Roberts, R.D. (Eds), International Handbook of Emotional Intelligence, Hogrefe & Huber, Cambridge, MA. Mouw, J.T. and Khanna, R.K. (1993), “Prediction of academic success: a review of the literature and some recommendations”, College Student Journal, Vol. 27, pp. 328-36. Oatley, K. and Jenkins, J.M. (2002), Understanding Emotions, Blackwell Publishing, Oxford. Ozga, J. and Sukhnandan, L. (1998), “Undergraduate non-completion: developing an explanatory model”, Higher Education Quarterly, Vol. 52 No. 3, pp. 316-33. Parry, G. (2002), “A short history of failure”, in Peelo, M. and Wareham, T. (Eds), Failing Students in Higher Education, Society for Research into Higher Education and Open University Press, Buckingham. Peelo, M. and Wareham, T. (2002), Failing Students in Higher Education, Society for Research into Higher Education and Open University Press, Buckingham.

Petrides, K.V. (2001-2006), Trait Emotional Intelligence, available online at: www.ioe.ac.uk/ schools/phd/kpetrides/teique1.htm (accessed 4 November 2005). Petrides, K.V. and Furnham, A. (2000), “On the dimensional structure of emotional intelligence”, Personality and Individual Differences, Vol. 29, pp. 313-20. Petrides, K.V. and Furnham, A. (2001), “Trait emotional intelligence: psychometric investigation with reference to established trait taxonomies”, European Journal of Personality, Vol. 15, pp. 425-48. Petrides, K.V., Frederickson, N. and Furnham, A. (2004), “The role of trait emotional intelligence in academic performance and deviant behaviour in school”, Personality and Individual Differences, Vol. 36, pp. 277-93. Reimann, N. (2004), “Calculating non-completion rates for modules on institution-wide language programmes: some observations on the nature of seemingly objective figures”, Journal of Further and Higher Education, Vol. 28 No. 2, pp. 139-52. Rickinson, B. and Rutherford, D. (1995), “Increasing undergraduate student retention rates”, British Journal of Guidance and Counselling, Vol. 23 No. 2, pp. 161-72. Schreiber, J.B. (2002), “Institutional and student factors and their influence on advanced mathematics achievement”, Journal of Educational Research, Vol. 95 No. 5, pp. 274-86. Willig, C. (2001), Introducing Qualitative Research in Psychology: Adventures in Theory and Method, Open University Press, Buckingham and Philadelphia, PA. Yorke, M. (1999), Leaving Early: Undergraduate Non-completion in Higher Education, Falmer Press, London. Further reading Petrides, K.V., Furnham, A. and Frederickson, N. (2004), “Emotional intelligence”, The Psychologist, Vol. 17 No. 10, pp. 574-7. About the author Emma Kingston, BSc, MSc, PGCert., is a Lecturer in Higher Education at the King’s Institute for Learning and Teaching, she is also completing her PhD in the School of Education at Roehampton University. During the course of her doctoral study she has obtained a postgraduate certificate in learning and teaching in higher education, and prior to this she received her master’s degree in research methods in psychology from the University of Surrey. Her research interests focus on emotional intelligence, assessment feedback, retention rates, and the integration of international students into UK higher education. She can be contacted at: [email protected]

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Durham University, Durham, UK, and

Carol Evans Michael Waring Loughborough University, Loughborough, UK Abstract Purpose – The purpose of this paper is to compare the cognitive styles of trainee teachers with their notions of differentiation and perceptions of its place/location within their teaching and learning during a PGCE programme of ITE. Design/methodology/approach – A total of 80 trainee teachers completed the Cognitive Style Index (CSI) at the beginning and at the end of their course. After completing the CSI measure trainees received instruction on cognitive styles. To assess their initial understanding and prior knowledge of differentiation, all trainees completed a questionnaire at the beginning and at the end of their course. Findings – At the outset rudimentary understandings of differentiation were found to be held by the trainees, as well as stylistic differences between the four style groupings. Gains in understanding of differentiation and the use of cognitive style in school were evident in all trainees. Moderate changes in style were evident, with all trainees becoming more intuitive over the course of the programme. Research limitations/implications – The sample size may be seen as a limitation in terms of generalisability. Practical implications – The predominant direction of cognitive style movement was from analytic to intuitive. The suggestion that cognitive style, while relatively fixed, is also something that can be developed is a feature which should offer encouragement to those developing university courses through interventions such as this. Originality/value – Teaching sessions on how cognitive styles can be used in the classroom were used to enhance trainee understandings of individual learning differences and increase awareness of one’s own style to facilitate understanding of differentiation. Keywords Functional differentiation, Teachers, Training Paper type Research paper

Education þ Training Vol. 50 No. 2, 2008 pp. 140-154 q Emerald Group Publishing Limited 0040-0912 DOI 10.1108/00400910810862128

Introduction In today’s increasingly diverse population, “personalized learning” has become embedded in the educational curriculum and notions of “learning how to learn” are clearly and firmly part of current and future English educational policy agendas (2020 Vision (DfES, 2007a); Making Good Progress (DfES, 2007b); QTS standards (TDA, 2007); DfES (2004, 2006)). However, such terms are considered by some to be ill-defined and vacuous concepts (Bates, 2005). One means by which to inform and offer greater clarification to elements of this individualising agenda would be to offer the exemplification of effective differentiation in a classroom context, along with an enhanced understanding and awareness of the development of trainee teachers’ theoretical insights about the nature of the teaching and learning process. With this in mind, the focus of the enquiry presented here is two fold: first to gain insights into trainee teachers’ conceptions of differentiation; and second to establish the relationship that cognitive style may have with such

conceptions of differentiation in order to appropriately scaffold their learning during a programme of initial teacher education (ITE). Differentiation and the trainee It is important to concentrate on the mechanisms by which those involved in teacher education can encourage and support trainee teachers to be able to critically reflect on what they do, be aware of bias in themselves and others, and how they question and cherish the implementation of theory in the teaching and learning process. Just as the intention is for all young people to be provided with an “understanding of how to learn, think creatively, take risks and handle change” (DfES, 2007b, p. 14), then so shouldn’t it be for trainee teachers? Hutchings et al. (2006, p. 87) asks some poignant questions in relation to this: “At what point in teacher training and professional development activity are trainees and teachers most receptive to theoretical insights about, for example, the nature of teaching and learning?” An important dimension of this will be recognising and developing the ability of the trainee to understand differentiation “of” and “in” learning. However, when attempting to address Hutchings et al.’s question certain factors need to be recognised: . the discrepancy, confusion and ambiguity in terminology employed by policy in schools compared to teacher education; and . exposure to “good” practice in terms of the extent, nature and timing of it during a one year Post Graduate Certificate in Education. Differentiation is often highlighted as an area in which trainee teachers have difficulties (Henderson, 2006). This could on the one hand be explained by a lack of experience of sound differentiation practices in their own experiences of learning, and on the other, by poor understandings and lack of clarity by practicing teachers of what differentiation is and how to achieve it (O’Brien and Guiney (2001; Babbage et al., 1999; Pettig, 2000; Scott et al., 1998 as cited in Westwood, 2005). Importantly, O’Brien (2000) highlights the need for differentiation to be seen as an inclusive concept and not as a reactive response to a child experiencing difficulty. In his differentiation model, O’Brien argues for the consideration of four interactive factors that impact on a learner’s ability to learn: pedagogical, emotional, cognitive and social, each of which should all be taken into account when planning effectively for differentiation. One way in which teachers and trainees can be more cognisant of their own learning needs, as well as those of their students, is through consideration of learning profiles comprising cognitive styles, learning styles and strategies (Rayner, 2000). Within this context, “Learning styles illustrate how a learner processes information and makes judgements about their own learning capabilities. Learning strategies relate to how the learner reacts to teacher decisions about how the learning environments is structured.” (O’Brien and Guiney, (2001, p. 63). Figure 1 illustrates the extent of such integration of differentiation in teaching and learning, representing the “fully integrated” (ideal differentiation) position on the right, shifting to a diluted and fundamentally impoverished “add on” position for differentiation on the left. To the left the centrality of the differentiation ethos of the

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Figure 1. Conceptions of differentiation in teaching

teacher has been completely eroded and replaced with it as merely a satellite concern/issue. The right hand side of Figure 1 identifies a situation where the teacher is fully aware of the individual learner’s needs through: accessing various forms of prior knowledge about the individual; fully understanding the theory related to cognitive styles and learning preferences; how knowledge on learning disabilities can be best employed for the benefit of all pupils; the role of cognitive neuroscience; and association with affective elements that impact on learning. Together these components enable the teacher to create conditions for learning allowing all to thrive. The more the teacher considers differentiation to be an appendage, something of an “add on” in their planning and preparation for teaching and learning, the more the awareness, flexibility, malleability, understanding, choice and challenge is reduced. In some instances this is completely removed from the (teaching) learning experience.

Cognitive style A cognitive styles approach and the use of instruments such as the cognitive styles analysis (CSI) Allinson and Hayes (1996) were adopted in this study to consider ways of differentiating learning in order to highlight and explore the “process” of learning and the integration of subject and pedagogical knowledge as advocated by a metacognitive approach. Cognitive style(s) are commonly described as characteristic modes of thinking, remembering, and problem-solving (Messick, 1984). They are seen as “stable . . . unchangeable, individual characteristics which partly control and organise more-fluid cognitive strategies.” (Schmeck, 1988, p. 176). The fixed nature of cognitive style(s) has been challenged in recent years (O’Malley and Charmot, 1990; Skehan, 1998; Adey et al., 1999; Driver, 2000; Sitko-Lutek et al., 2000; Armstrong, 2002; Thies, 2003). Among those that suggest cognitive style is malleable, questions have been raised over the degree of malleability (Armstrong, 2002) and also whether some individuals are more amenable to style flexibility than others (Evans, 2004). The suggestion that style modification may occur as a result of considered training is something that teacher training programmes should contemplate in regards to developing the potential of their programmes (O’Malley and Charmot, 1990; Skehan, 1998; Evans and Waring, 2006; Rosenfeld and Rosenfeld, 2004). Aims of the study The aim of the study was to compare the cognitive styles of trainee teachers with their notions of differentiation and perceptions of its place/location within their teaching and learning during a PGCE programme of ITE. By exploring and identifying such relationships ITE programmes can be refined not only in relation to the nature, delivery and assessment of curriculum programming, but in terms of enhancing the most meaningful integration of theory and practice. Method A total of 80 trainee teachers (males ¼ 33; females ¼ 47) aged between 21 and 55 years (mean 24 years) enrolled on a one year Postgraduate Certificate in Education (PGCE) programme completed the Cognitive Style Index (CSI) (Allinson and Hayes, 1996) at the beginning of their course, 89 per cent of whom completed a re-test nine months later at the end of their course. Justification for the selection of the CSI was three fold: it is one of the most reliable and valid measures, possessing good psychometric credentials (Coffield et al., 2004); it is relatively easy and efficient to administer; and there is considered to be no carry-over effect from repeated use of it (Zhang et al., 2005). The CSI scores in this study were calculated using a revised scoring method advocated by Hodgkinson and Sadler-Smith (2003) with analysis and intuition identified as coexisting complementary modes of information processing. The original test comprises 38 statements scored in a trichotomous scale (true; uncertain; false). Using the revised scoring method both analytic and intuitive items are scored positively on two separate scales: true ¼ 2; uncertain ¼ 1 and false ¼ 0). Thus 21 statements measure analysis, resulting in a maximum score of 42 and a minimum of 0; 17 statements measure intuition, giving a maximum score of 34 and minimum of 0. Intuition scores were later recalculated out of 42 to enable direct comparison with

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analysis scores. Using mean scores for analysis and intuition dimensions, it was possible to divide the data into four groupings: (1) high analysis-low intuition; (2) high analysis-high intuition; (3) low analysis-low intuition; and (4) low analysis-high intuition. After completing the initial CSI measure at the beginning of the programme, all trainees received instruction on cognitive styles including key note lectures, follow up group discussions and one to one interviews. To assess their initial understanding and prior knowledge of differentiation, all trainees completed a questionnaire at the beginning and another towards the end of their one year course, so as to assess any development in understanding (71 out of 80 trainees completed both questionnaires). Using stratified sampling procedures to select trainees with differing style profiles, focus interviews were employed to explore issues raised during the teaching sessions and articulated in the questionnaire responses. Responses were coded using content analysis procedures with two researchers independently verifying key features from the data. Data analysis Test-re-test scores for the CSI on both intuition and analytic dimensions demonstrate acceptable reliability (Nunnally, 1978). For intuition: test-re-test value of r ¼ 0.67; p ¼ 0.00; the 95 per cent confidence interval was 0.49 to 0.77. For analysis: r ¼ 0.65, p ¼ 0.00; the 95 per cent confidence interval was 0.52 to 0.78. Analysis scores were significantly higher than intuition scores on both test and re-test. Mean analysis 1 ¼ 29.8; SD ¼ 6.2; N ¼ 80; mean analysis 2 ¼ 28.9; SD ¼ 6.9; N ¼ 69 (86 per cent of original sample).Mean intuition 1 ¼ 21.3; SD ¼ 8.2; N ¼ 80; mean intuition 2 ¼ 22.8; SD ¼ 8.1; N ¼ 69. Using mixed between-within subjects ANOVA, there was not a statistically significant effect for time, suggesting little change in analysis scores over time (Wilks’ Lambda ¼ 0.98, F ¼ (1, 67) ¼ 1.067, p ¼ 0.305, eta squared ¼ 0.16); and there was no significant interaction effect with gender (Wilks’ Lambda ¼ 0.99, F ¼ (1, 67) ¼ 0.85, p ¼ 0.360) suggesting that the nature of change for the males and females was similar. In relation to changes in intuition score over time, using mixed between-within subjects ANOVA, there was no statistically significant effect for time suggesting little change in intuition scores over time (Wilks’ Lambda ¼ 0.98, F ¼ (1, 67) ¼ 1.61, p ¼ 0.208, eta squared ¼ 0.024). There was, however, a statistically significant interaction effect with gender (Wilks’ Lambda ¼ 0.931, F ¼ (1, 67) ¼ 4.94, p ¼ 0.030, eta squared 0.069 (moderate)) suggesting that the nature of change for males and females was quite different. Using mean scores, the intuition score for males declined slightly over the duration of the intervention whereas the female score which was much lower at the start of the course, increased significantly following the intervention. Non-significant results using mixed between groups ANOVA need to be interpreted with caution given the small size of study and insufficient power recordings assigned to both analysis and intuition tests 0.18 and 0.24 respectively. At Time 1 – the beginning of the course (see Table I), analytic style dominance was particularly evident with 45 per cent of trainees exhibiting Style 1 characteristics (high analysis, low intuition) although 25 per cent did fall into Style 4 (low analysis, high

intuition). By the end of the study there was evidence of movement towards higher levels of intuition among the cohort. In relation to style groupings and gender certain patterns were evident. At Time 1, both the majority of males and females (42 per cent and 47 per cent respectively) fell into Style 1, however a greater percentage of males (30 per cent to 21 per cent of females) were Style 4 (low analysis, high intuition). At Time 1 (the beginning of the course), there was not a statistically significant difference between males and females in relation to analysis scores (see Table II), whereas there was a statistically significant difference in relation to intuition scores between the two sexes: t ¼ 2.12; df ¼ 78; p ¼ 0.037; moderate effect size of 0.058. This supports previous findings (Allinson and Hayes, 1996; Sadler-Smith et al., 2000; Murphy et al., 1998). However, following training in cognitive style, by the end of the course (Time 2), there was no statistically significant difference between male and female scores on either analysis or intuition, a finding in common with that of Zhang et al. (2005). Using the two sets of questionnaire data, individual trainee scores on analysis and intuition and written and verbal responses from the trainees, there was greater movement in cognitive style than might be suggested by the ANOVA results. Of the Style 1 trainees (high analysis and low intuition) 19 per cent moved to Style 2 (higher intuition) with 65 per cent remaining the same style; For Style 2 trainees (high analysis and high intuition) 50 per cent stayed the same and over 33 per cent increased their intuition scores and lowered their analysis scores moving them to Style 4; Only 20 per cent of Style 3 remained in this style (low analysis low intuition) with over 50 per cent moving to Style 4 (increased intuition) and over 33 per cent moving to style 1 (higher analysis); Style 4 trainees(low analysis high intuition) 65 per cent remained the same style with 24 per cent moving to Style 2 and therefore, raising their analysis scores. By the end of the study, the biggest change was in the distribution of females in Style 4 which increased from 21 per cent to 38 per cent. In terms of percentage change,

2 3 4

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Time 1 Males Females Time 2 Males Females n Per cent n ¼ 33 n ¼ 47 n Per cent n ¼ 27 n ¼ 42

Style Style type 1

Trainee teachers’ cognitive styles

High analysis low intuition High analysis high intuition Low analysis low intuition Low analysis high intuition

36

45

42%

47%

25

36.2

33%

38%

14

17.5

21%

15%

15

21.7

30%

17%

10

12.5

6%

17%

6

8.7

11%

7%

20

25

30%

21%

23

33.3

26%

38%

Gender

n

Analysis 1

SD

Analysis 2

SD

Intuition 1

SD

Intuition 2

SD

Males Females Total

27 42 69

29.3 30.07 29.8

6.9 5.7 6.2

29.2 28.8 28.9

6.98 7.07 6.98

23.7 19.8 21.3

7.4 8.4 8.2

22.9 22.7 22.8

7.5 8.5 8.1

Table I. Style groups and changes in style over time

Table II. Gender variations – mean analysis and intuition scores

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males demonstrated bigger increases in relation to Style 2, where by the end of the study 30 per cent of males exhibited high analysis and high intuition scores compared to 21 per cent at the outset. Trainee initial understandings of their own learning At the beginning of the one year PGCE course, the majority of trainees had a very limited vocabulary to describe their own learning and had little awareness of approaches used in the classroom to facilitate learning. What understandings there were predominantly focused on VAK (visual, auditory and kinaesthetic along with preferences for active learning). Visual learning as a dominant mode of learning was a common response among many trainees (34 per cent) and especially true of Style 2 trainees (high analysis (HA) and high intuition (HI)). of all the trainees 40 per cent placed high emphasis on “active” learning (trying out ideas/practical work). Consolidation of learning by rehearsing, re-writing information was dominant as a form of learning for both Styles 1 and 3. Very few trainees referred to group work and discussion as ways of encouraging learning. Trainees also rated their ability/preference for certain learning approaches using a five-point Likert scale (strongly agree to strongly disagree respectively). Table III shows the mean scores for selected statements and summarises the key significant differences using one-way ANOVA. Interpreting these results with caution, they do provide a form of triangulation and support for the statements obtained in the written questionnaires, in interviews and from the trainees in teaching sessions. In order to interpret the mean scores in Table III, a score of 1 equates to a trainee who strongly agrees and score of 5 equates to a trainee who strongly disagrees with the statement. Thus from Table III significant differences between the four styles in relation to their perceived preferences and approaches in learning are identified. There were significant differences between those who were highly analytic (Style 1) and those who were highly intuitive (Style 4) which supports the literature. Style 1 trainees purported to be able to organise their workload more effectively than Style 4 trainees. They also showed a greater preference for a logical and structured approach to work, being more in favour of clear outlines and overviews to sessions. In addition and in alignment with the reported tendency of analytics to process in detail (Evans, 2004), they were more likely to take more time to reach a decision when presented with information. Style 2 and 4 trainees (both with high intuition) were most likely to favour less structured approaches and favoured practical approaches and more informal learning supporting previous findings (Evans and Waring, 2006). Style 4 (low analysis, high Intuition) were also less likely to request outlines to sessions and purported to be most able to multi-task with Style 3 trainees (low analysis and low intuition) being least in favour of multi-tasking, group work and informal learning and most in favour of a lecture format facilitating a transmitting style of learning. Trainee initial understandings of differentiation Approximately 25 per cent of all trainees viewed differentiation as “learning in different ways” and “catering for all”, however, articulation of what this actually involved was limited. Styles 1, 2 and 4 emphasised abilities. Varying teaching style was a particular focus for Style 1 (32 per cent) and Style 2 (20 per cent) trainees. A total

3.52 (0.9)

Ability to multi-task

Prefer informal learning

Prefer group work

Favour practical based learning

Require a clear outline and overview of learning

Reflect and consider all options before arriving at decision

1.42 (0.51) 1.86 (0.77) 2.07 (0.99)

2 (0.79)

1.5 (0.65)

2.28 (0.82)

2.57 (0.85)

3.57 (1.22)

2.36 (0.84)

14

Style 2 – high analysis high intuition

1.86 (0.87) 2.67 (0.96)

1.36 (0.49)

2.2 (0.81)

1.77 (0.79)

1.89 (0.74)

Organisational skills

Require logical, highly structured sequence

36

n

Perceived learning

Style 1 – high analysis low intuition

2.4 (0.84)

2 (0.82) 2.9 (1.09)

1.8 (0.92)

2.8 (0.92)

2 (0.67)

4.10 (0.87)

2.3 (0.94)

10

Style 3 – low analysis low intuition

1.56 (0.60)

1.35 (0.59) 2.2 (0.69)

2.05 (0.83)

3.05 (0.95)

2.6 (0.99)

2.9 (1.07)

2.85 (1.63)

20

Style 4 – low analysis high intuition

2.9

4.04

3.2

4.9

4.1

5.5

3.5

2.6

0.04

0.010

0.03

0.004

0.009

0.007

0.019

0.056

One way Anova Significance F p

1 and 2 2 and 3 3 and 4

0.10 moderate

1 and 4

1 and 4

1 and 2, 4

3 and 4

1 and 4

HSD Tukey significant differences between groups

0.14 large

0.11 moderate

0.16 large

0.14 large

0.18 large

0.12 mod-large

0.09 moderate

Effect size Eta square

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Table III. Significant learning preferences of the four styles as demonstrated by means

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of 30 per cent of Styles 2 and 4 mentioned using different methods in the classroom, but did so in ambiguous terms. Planning for differentiation, trainees, as might be expected at this stage in their development (Bullough et al., 1991) had limited understandings. Notions of differentiation that were explored centred on “add on models”. For example, where something different is “done for those of higher ability”. Although 29 per cent of trainees acknowledged the importance of assessing the prior knowledge of their students in order to plan more effectively, 90 per cent of Style 3 trainees appeared completely unaware of this. Very little mention among the trainees was made regarding how groupings of students could be used to differentiate learning. Similarly any awareness of special educational needs (SEN) issues and how good practice in SEN could be used with all learners was lacking for the majority (73 per cent) of trainees. Accommodating the differing needs of individual students i.e. “varying the task(s)” within a lesson was mentioned most by Style 3 (44 per cent) and least by Style 1 (31 per cent) trainees. Using different teaching and learning styles was cited most highly by Style 2 trainees (54 per cent), with Style 3 trainees least likely to refer to using different learning styles (11 per cent) and most likely to talk about resource needs (60 per cent). Styles 1 and 4 emphasised the importance of pupils’ prior knowledge, with 38 per cent of Style 1 trainees focusing on the importance of the appropriateness of the work set. At the end of the programme 59 per cent of all trainees considered the cognitive style training highly useful in enabling them to understand differentiation needs among pupils. However, there was great variation evident among styles, e.g. 80 per cent of Style 2 (HA and HI) compared to 40 per cent of Style 3 (LA and LI) found them beneficial; Style 3 found it considerably more difficult to see how a study of cognitive styles was relevant for teaching. This provides a challenge for university tutors. Styles 3 (LA and LI) and 4 (HI and LA) found the first cognitive style training session, which offered a comprehensive overview of cognitive styles, to be the most useful. Of those identified, 75 per cent as having high levels of both intuition and analysis (Style 2) found the university-based intervention the most useful. Trainees who scored highly on intuition (styles 2 and 4) favoured more interaction. Trainees with high analytical and low intuitive scores were most likely to feel overloaded with information; application to examples was important for over 39 per cent of all trainees, especially for Style 2 and least important to Style 4. Impact of the university-based cognitive styles intervention While the majority (90 per cent) of all trainees had been aware of learning styles at the beginning of the course, after the university-based intervention, ideas about how to implement differentiation practices were predominantly simplified by trainees in-line with their experiences in schools and linked to notions of visual, auditory and kinaesthetic learning (VAK) with little reference to any other concrete ideas. All trainees exhibited greater awareness of the needs of differing groups of children with most comments relating to ability, gender and SEN issues. However, little reference was made to gifted and talented, ethnic minority or to the variable social and emotional needs of pupils. Pedagogically speaking, the trainees were more aware of the importance of assessing the prior knowledge of pupils in order to plan for effective teaching. The majority of trainees were also aware of the need to vary their own styles of teaching and assessment.

Style group variations were evident. Style 1 trainees were the most likely to mention catering for all styles of teaching; 61 per cent mentioned their own need to better understand learning needs; 43 per cent the importance of varying tasks and variety; 30 per cent mentioned use of VAK; 83 per cent said they would vary their own teaching style; 74 per cent had a preference for varying whole class, group and individual work; 35 per cent stressed varying questioning; and they were the group most likely to want to marry assessment and learning preferences. Style 2 trainees were more likely to raise behaviour issues, the need for challenge and provision particularly for the most able students. Style 2 placed great emphasis on catering for all styles of learning; 25 per cent mentioned VAK; 42 per cent need to vary teaching style; less emphasis on using a mix of whole class, individual and group work; 33 per cent variety in assessment; importance of both focused, open and varied questioning in the classroom. Style 3 trainees were most likely to raise class size as an issue and barrier to effective differentiation. 20 per cent mentioned VAK; use of varied resources were seen as important; over 70 per cent mentioned varying teaching styles, but none had a developed notion of what this was; 20 per cent talked about fast delivery; 20 per cent felt tasks should be matched to different styles of learner; 40 per cent favoured mixing whole class, group and individual activities with no notion of interaction or types of questioning; 20 per cent felt they needed to reflect more on own teaching to also understand all learning needs; 70 per cent felt assessment should cover all needs. Style 4 trainees were most aware of SEN; importance of challenge; varying methods. 41 per cent of them emphasised that planning needed to cater for all learning types and use of a variety of tasks; 24 per cent mentioned VAK; 53 per cent acknowledged the importance of vary teaching style; 24 per cent favoured group work (also favoured by Style 1); 41 per cent favoured a mix of whole class, group and individual work; 53 per cent wanted variety in assessment and open questioning in the classroom. Individual learning points A total of 22 per cent of all trainees following the university based intervention highlighted an increased personal understanding of their own learning and the impact it might have on their pupils: I like all the information at the start of a lesson . . . as a result, my teaching has meant that I sometimes give pupils too much information at the start (Style 4 – LA-HI). I have an understanding that I have a specific way of learning that reflects upon my pupils (Style 3 – LA-LI). Knowing how I learn has helped me to understand how others learn and appreciate that this may differ (Style 4 – LA-HI). It [the training] enabled the trainees to put themselves in the position of the pupils and identify how each individual has their own preferred learning profile (Style 2 – HA-HI). It makes you realise why certain things happen on placement and it gives you more tools to add to your box (Style 1 – HA-LI). It helped me to understand my learning but also how other people learn. I know to vary my delivery and presentation of information in school (Style 1 – HA-LI).

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While 28 per cent of Style 1 trainees said they felt they could apply the ideas to their teaching easily, 31 per cent of Style 1 trainees felt overloaded with information, preferring more incremental delivery of the ideas in the university-based sessions. Across all style groups 35 per cent of trainees welcomed greater application to specific examples. Style 2 and 4 (both high on intuition) favoured more interaction in university-based learning sessions. Of the trainees, 14 per cent were negative about the training, these were predominantly trainees who exhibited low analysis and intuition scores before and after the university-based intervention (i.e. Style 3). These trainees were less able to understand how they themselves learnt, however, they favoured repetition as a method of learning. This style also struggled more in relation to applying the ideas about cognitive style to their teaching. The written statements of the trainees were analysed using Marton et al. (1993) and Sa¨ljo¨’s (1979) conceptions of learning to identify more developed levels of understanding among the trainees. A total of 4214 per cent of Style 2 and 3514 per cent of Style 4 trainees exhibited greater levels of personal understanding of their own learning in their responses compared to 1614 per cent of Style 1 and 814 per cent of Style 3 trainees. Style 1 trainees, while not exhibiting as great an understanding of their own learning paradoxically were able to discuss at length how they would implement ideas in practice. Discussion Results suggest that the majority of trainees found the intervention helpful and a useful starting point to enable them to engage with the process of learning. In assessing trainees initial understandings of differentiation, it was apparent that trainees’ understanding was limited, raising questions as to how best develop awareness and a philosophical commitment to enabling all learners to learn (O’Brien and Guiney, 2001). The nature of the curriculum and methods of instruction that trainee teachers have been exposed to throughout the different phases of their own educational history at school and university may be instrumental in creating highly analytical teachers. In this study, trainees did initially demonstrate higher analysis than intuition scores, however, there was evidence of cognitive style movement, albeit greater for some than others. The predominant direction of cognitive style movement was from analytic to intuitive which replicates previous research findings (Evans and Waring, 2006) and supports Gregory (2000) who argues that awareness of intuition can be raised through training interventions. There is considerable debate surrounding the view that teachers need to develop both intuitive and analytical skills if they are to fully cater for the needs of all learners (Atkinson and Claxton, 2000). O’Brien and Guiney (2001, p. 56) take this further and argue that intuitive and reflective skills are essential if a teacher is to be able to “see beyond the superficial and consider what can be done to enable a learner to learn.” The suggestion that cognitive style while relatively fixed is also something that can be developed, is a feature which should offer encouragement to those developing university courses through interventions such as this. Initial gender differences were highlighted with males demonstrating higher intuition scores as demonstrated in the literature (This supports previous findings (Allinson and Hayes, 1996; Sadler-Smith et al., 2000; Murphy et al., 1998). It is interesting to note that following the intervention, there were no statistically

significant differences between male and female intuition scores suggesting that the training did enable the female trainees to develop their intuitive thinking. This study highlights the value of a metacognitive approach to differentiation whereby learners who gain greater understanding of their own learning are then more predisposed to being able to consider differing learning perspectives. This approach requires teachers to gain an in-depth understanding of child development, learning outcomes, assessment strategies of and for learning, as well as being flexible and effectively using time and resources (Tomlinson, 2004). Such thinking however, needs to be grounded by exposure to good practice on an on-going incremental, cumulative and measured basis. In addition to enabling sufficient time for refection and analysis, trainees need as much exposure to good practice in a school context as is possible during their placements, whether this is through: the use of “leading lights” (expert teachers/expert schools); changes in Initial Teacher Education to encourage a more problem-solving/demonstration approach; peer support programmes; online problem-solving exercises; or vicarious learning – demonstration of others thinking and doing). In addition, on going continuing professional development throughout a teacher’s career must be increasingly attuned to providing higher levels of support if good differentiation within schools is to become a reality. This study also demonstrated, through the use of a complex rather than unitary conceptualisation of style (the CSI), that it may be possible for an individual to be both analytic and intuitive and to be able to develop strategies to address any inherent bias towards one style or another (Evans and Sadler-Smith, 2006). In addition, while differing style priorities were evident in this study, caution is needed in interpreting these as other contextual factors may be relevant depending on subject and course, call for further studies to ascertain whether such findings are generalisable. Conclusion The intervention in this study enabled trainees to think more carefully about their own and others’ learning and thus provided a useful tool/mechanism that would fit with Claxton’s (1999, 2006) concept of “expanding the capacity to learn.” The importance of assessing variable needs of trainees in order to allow them to access theory is also brought to the fore by this study. The integration of theory and practice in a manner that is meaningful for a trainee is a key issue requiring flexibility in the teaching and learning process. Cognitive style and differentiation provide a convenient marriage as both enable autonomy in allowing the learner and teacher to gain self-awareness, provide challenge and promote awareness of choice, resulting in more enriched understandings. By encouraging trainees to consider their own learning processes and biases in planning, delivery and assessment they (albeit at an early stage in their career development) are more receptive and able to consider different approaches. Similarly a critically informed use of a cognitive styles approach can enable trainers/tutors to consider the initial starting points of their trainees and to plan accordingly for these through the use of specific strategies to develop both analytic and intuitive capacity. References Adey, P., Fairbrother, R., Wiliam, D., Johnson, B. and Jones, C. (1999), Learning Styles and Strategies: A Review of Research, King’s College London, London. Allinson, C.W. and Hayes, J. (1996), “The Cognitive Styles Index: a measure of intuition-analysis for organisational research”, Journal of Management Studies, Vol. 33 No. 1, pp. 119-35.

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Armstrong, S.J. (2002), “Effects of cognitive style on the quality of research supervision”, in Francis, A., Armstrong, S., Graff, M., Hill, J., Rayner, S., Sadler-Smith, E. and Spicer, D. (Eds), Proceedings of the 7th Annual Conference of the European Learning Styles Information Network, Ghent University, Belgium, 26-28 June 2002, pp. 13-24. Atkinson, T. and Claxton, G. (2000), “The intuitive practitioner: a critical overview”, The Intuitive Practitioner, Open University Press, Maidenhead. Babbage, R., Byers, B. and Redding, H. (1999), Approaches to Teaching and Learning: Including Pupils with Learning Difficulties, Fulton, London. Bates, R. (2005), “On the future of teacher education: challenges, context and content”, Journal of Education for Teaching, Vol. 31 No. 4, pp. 301-5. Bullough, R.V., Knowles, J.G. and Crow, N.A. (1991), Emerging as a Teacher, Routledge, London. Claxton, G. (1999), Wise Up: The Challenge of Lifelong Learning, Bloomsbury, London. Claxton, G. (2006), “Expanding the capacity to learn: a new end for education?”, keynote speech, British Educational Research Association Annual Conference, University of Warwick, 6-9 September 2005. Coffield, F., Moseley, D., Hall, E. and Ecclestone, K. (2004), Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review, Learning and Skills Research Centre (LSDA), London. Department for Education and Skills (DfES) (2004), Five Year Strategy for Children and Learners, Department for Education and Skills, London. Department for Education and Skills (DfES) (2006), Five Year Strategy for Children and Learners: Maintaining the Excellent Progress, Department for Education and Skills, London. Department for Education and Skills (DfES) (2007a), 2020 Vision – Report of the Teaching and Learning in 2020 Review Group, Department for Education and Skills, London. Department for Education and Skills (DfES) (2007b), Making Good Progress. How Can We Help Every Pupil to Make Good Progress at School?, Department for Education and Skills, London. Driver, M.J. (2000), “Decision style: past, present and future research”, in Riding, R. and Rayner, S. (Eds), International Perspectives on Individual Differences, Volume 1, Cognitive Styles, Ablex, Stamford, CT. Evans, C. (2004), “Exploring the relationship between cognitive style and teaching style”, Educational Psychology, Vol. 24 No. 4, pp. 509-30. Evans, C. and Sadler-Smith, E. (2006), “Learning styles”, Education + Training, Vol. 48 No. 2. Evans, C. and Waring, M. (2006), “Towards inclusive teacher education: sensitising individuals to how they learn”, Educational Psychology, Vol. 26 No. 4, pp. 499-519. Gregory, G. (2000), “Developing intuition through management education”, in Atkinson, T. and Claxton, G. (Eds), The Intuitive Practitioner on the Value of not Always Knowing What One Is Doing, Open University Press, Maidenhead, pp. 182-95. Henderson, D. (2006), “Student teachers can’t cram it all in”, Times Educational Supplement, 10 February, p. 2006. Hodgkinson, G.P. and Sadler-Smith, E. (2003), “Complex or unitary? A critique and empirical reassessment of the Cognitive Style Index”, Journal of Occupational and Organisational Psychology, Vol. 76, pp. 243-68. Hutchings, M., Maylor, U., Mendick, H., Menter, I. and Smart, S. (2006), An Evaluation of Innovative Approaches to Teacher Training on the Teach First Programme, Final report to the Training and Development Agency for Schools, Institute for Policy Studies in Education, London, pp. 1-89.

Marton, F., Dall’Alba, G. and Beaty, E. (1993), Conceptions of Learning, 2nd ed., Scottish Academic Press, Edinburgh. Messick, S. (1984), “The nature of cognitive styles: problems and promise in educational practice”, Educational Psychologist, Vol. 19, pp. 59-74. Murphy, H.J., Kelleher, W.E., Doucette, P. and Young, J.D. (1998), “Test-retest reliability and construct validity of the Cognitive Style Index for business undergraduates”, Psychological Reports, Vol. 82, pp. 595-600. Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill, New York, NY. O’Brien, T. (2000), “Providing inclusive differentiation”, in Benton, P. and O’Brien, T. (Eds), Special Needs and the Beginning Teacher, Continuum, London. O’Brien, T. and Guiney, D. (2001), Differentiation in Teaching and Learning, Continuum, London. O’Malley, J. and Charmot, A. (1990), Learning Strategies in Second Language Acquisition, Cambridge University Press, Cambridge. Pettig, K.L. (2000), “On the road to differentiated practice”, Educational Leadership, Vol. 58 No. 1, pp. 14-18. Rayner, S. (2000), “Reconstructing style differences in thinking and learning: profiling learning performance”, in Riding, R. and Rayner, S. (Eds), International Perspectives on Individual Differences, Volume 1, Cognitive Styles, Ablex, Stamford, CT. Rosenfeld, M. and Rosenfeld, S. (2004), “Developing teacher sensitivity to individual learning differences”, Educational Psychology, Vol. 24 No. 4, pp. 465-87. Sadler-Smith, E., Spicer, D.P. and Tsang, F. (2000), “The Cognitive Style Index: a replication and extension”, British Journal of Management, Vol. 11, pp. 175-81. Sa¨ljo¨, R. (1979), Learning in the Learner’s Perspective I – Some Common Sense Perceptions, University of Go¨teborg, Go¨teborg. Schmeck, R.R. (Ed.) (1988), Styles and Strategies of Learning, Plenum, New York, NY. Scott, B.J., Vitale, M.R. and Masten, W.G. (1998), “Implementing instructional adaptations for students with learning disabilities in inclusive classrooms”, Remedial and Special Education, Vol. 19 No. 2, pp. 106-19. Sitko-Lutek, A., Rakowska, A. and Hill, J. (2000), “To match or not to match? The conundrum of management education for reflective-analytical Polish managers”, in Armstrong, S., Francis, A., Graff, M., Hill, J., Rayner, S., Sadler-Smith, E. and Spicer, D. (Eds), Proceedings of the 5th Annual Conference of the European Learning Styles Information Network, Business School, University of Hertfordshire, Hatfield, 26-27 June 2000, pp. 261-77. Skehan, P. (1998), A Cognitive Approach to Language Learning, Oxford University Press, Oxford. Thies, A. (2003), “Connections, neuropsychology, neuroscience, and learning style”, in Armstrong, S., Graff, M., Hill, J., Rayner, S., Sadler-Smith, E. and Spicer, D. (Eds), Bridging Theory and Practice, European Learning Styles Information Network (ELSIN), University of Hull, Hull, 30 June-2 July 2003, pp. 608-12. Tomlinson, C.A. (2004), “Sharing responsibility for differentiating instruction”, Roeper Review, Vol. 26 No. 4, pp. 188-9. Training and Development Agency for Schools (TDA) (2007), Professional Standards for Teachers Qualified Teacher Status, Training and Development Agency for Schools, London. Westwood, P. (2005), “Adapting curriculum and instruction”, in Topping, K. and Maloney, S. (Eds), The Routledge Falmer Reader in Inclusive Education, Routledge Falmer, London, pp. 145-59.

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Zhang, C.L., Allison, C.W. and Hayes, J. (2005), “Change and continuity in intuition – analysis: an examination of the malleability of cognitive style”, in Armstrong, S., Evans, C., Graff, M., Peterson, E., Rayner, S. and Sadler-Smith, E. (Eds), European Learning Styles Information Network 10th Annual International Conference 2005, School of Management, University of Surrey, Guildford, 13-15 June.

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Further reading Clare, J.D. (2004), “Differentiation”, at Greenfield School, available at: www.greenfield.durham. sch.uk/differentiation.htm (accessed 20 June 2006). Hall, T. (2002), Differentiated Instruction, National Center on Accessing the General Curriculum, Wakefield, MA, available at: www.cast.org/publications/ncac/ncac_diffinstruc.html (accessed 2 May 2007). National Educational Research Forum (NERF) (2005), Evidence for Teaching and Learning, No. 4, Autumn, p. 12. National Educational Research Forum (NERF) (2006), Evidence for Teaching and Learning, No. 6, Summer, pp. 1-16. Oaksford, L. and Jones, L. (2001), Differentiated Instruction Abstract, Leon County Schools, Tallahassee, FL. Robinson, M. (2005), “Uncertainty and innovation”, Journal of Education of Teaching, Vol. 31 No. 4, pp. 263-4. Tomlinson, C.A. (2005), “Grading and differentiation: paradox or good practice?”, Theory into Practice, Vol. 44 No. 3, pp. 262-9. Westwood, P.S. (2001), “Differentiation as a strategy for inclusive classroom practice: some difficulties identified”, Australian Journal of Learning Disabilities, Vol. 6 No. 1, pp. 5-11. About the authors Carol Evans is currently an Education Adviser at the Kent, Surrey and Sussex Postgraduate Deanery, University of London, she is moving to become Deputy Director of Learning and Teaching at the Institute of Education, University of London. Prior to teaching at Durham University before this, she worked in schools for over 22 years in a variety of roles from classroom teacher to senior manager. Her research interests include: improving conditions for learning in the classroom and teacher education programmes; and cognitive and learning styles. She is also an Honorary Fellow of Durham University and Vice President of the European Learning Styles Information Network (ELSIN). She is the corresponding author and can be contacted at: [email protected] Michael Waring is currently Senior Lecturer of Physical Education in the School of Sport and Exercise Sciences at Loughborough University. He is Director of the MSc in Physical Education and contributes to the Secondary PE PGCE programme. His research interests include teaching and learning, critique and development of grounded theory methodology and the level and determinants of young peoples’ involvement in physical activity.

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Implementation of learning styles ofImplementation learning styles at the teacher level Tine Nielsen Copenhagen Business School, Copenhagen, Denmark

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Abstract Purpose – The purpose of this paper is to present, try out, and evaluate a strategy for implementation of learning and teaching styles at the teacher level. Design/methodology/approach – The study takes a qualitative approach to evaluating the short-term and long-term effects of a workshop on teaching and learning styles with regard to changing teachers’ implicit beliefs and teaching practice. Findings – Fourteen months after a two-day workshop on learning and teaching styles, teachers’ implicit beliefs about learning and teaching remain explicit and their teaching practice has changed towards a higher degree of differentiation as a result of the workshop. Practical implications – The paper demonstrates that it is possible to change experienced teachers’ teaching practice to a higher degree of differentiation with a two-day workshop. Originality/value – The paper provides knowledge on how to change in-service teachers’ implicit beliefs and how to affect their teaching practice to making use of of learning and teaching styles in their teaching practice. Keywords Learning styles, Teaching styles, Training, Core beliefs, Adult education Paper type Research paper

Introduction Research into the learning styles of adults is vast and conducted within a growing number of theories – for example Vermunt’s (1992) theory of learning styles, Kolb’s (1984) theory of experiential learning, and Sternberg’s (1997) theory of mental self-government, just to mention a few. Most of this research is cross-sectional and concerned with the learning styles of students in higher education and how these are dependent on gender, age, university major, and personality (for example Severiens, 1997; Brew, 2002; Nielsen, 2005a) A smaller number of studies, primarily within the theories of Vermunt and Sternberg, are longitudinal and have shown that learning styles of university students are not stable over time, rather they are socialized during the course of study (for example Vermetten et al., 1999; Busato et al., 1998; Nielsen, 2005a). The knowledge on student styles obtained from these lines of research can be utilized when implementation of learning styles in adult education is concerned with teachers considering diversity of student styles in general, at different levels of study and in different subjects (Nielsen, 2005a). This type of implementation of learning styles in adult (and other) education is often discussed in terms of matching or mismatching teachers’ teaching styles to students’ learning styles (for example Hyman and Rosoff, 1984; Beck, 2001, Zhang, 2006), and should, therefore draw on research concerned with the relationships between students’ learning styles and teachers’ teaching styles (for example McMillin, 1999; Sternberg and Grigorenko, 1995; Zhang, 2006).

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Before the above level of implementation of learning styles in adult education can be pursued, there is a prior level of implementation to consider, namely the teacher level. That is, implementation of learning (and teaching) styles in adult education in ways that will enable the teachers to consciously choose and interchange between strategies of matching and/or mismatching of their own styles to the student styles. In other words, implementation at the teacher level should qualify teachers to being able to differentiate their teaching so as to accommodate the individual needs of the students, whether this is done by matching or mismatching of styles or other means of conscious variation of teaching styles. The paper will present a strategy of implementation within the theoretical framework of Sternberg’s theory of mental self-government in a Danish adaptation (Sternberg, 1997; Nielsen, 2005a) using the Danish Self-assessment Learning Styles Inventory (Nielsen, 2005b). The implementations took the form of teacher workshops designed to facilitate stylistic and strategic flexibility through working actively with knowledge acquisition and development of insight. One such workshop with Danish high school teachers is evaluated with regard to self-reported short term and long term effects on the teachers’ thinking about their own practice as well as their actual teaching practice. Learning and teaching styles as implicit beliefs In the present study, learning and teaching styles are defined as a profile of thinking styles describing the individual’s preferred ways of thinking when learning or teaching, with the preferred ways of thinking representing different ways of perceiving and handling problems and situations in the given context of learning/teaching (Sternberg, 1997; Nielsen, 2005a). Both learning and teaching styles are, according to Sternberg (1997), socialised throughout the lifespan of the individual, and particularly by the individual’s experience with the learning-teaching situation on her journey through the educational system (e.g. secondary socialization, Katzenelson, 1994). On a general level, the individual’s experiences with the learning-teaching situation as well as her learning styles become part of the individual’s larger system of implicit knowledge and beliefs about human nature, learning, teaching, etc. Transferring the above specifically to teachers means that a teacher’s system of implicit knowledge and beliefs about teaching and learning includes the teacher’s own experiences with teaching primarily in the role of learner, the teacher’s learning styles and the teacher’s teaching styles (top part of Figure 1). These three elements of the teacher’s implicit belief system mutually affect one another. In this way, the teacher’s preferences for learning are affected by her experiences with teaching, and in turn the teacher’s preferences for learning affect how teaching is experienced – the relationship between the teacher’s experiences and her teaching styles is similarly affected. Furthermore, it is proposed that there is a mutual effect between the teacher’s learning and teaching styles: for novice teachers in the sense that she will transfer her own preferences for learning to her preferences for teaching, for non-novice teachers in the sense that as her preferences for teaching develop this will to some extent affect her preferences for learning and vice versa. Research has shown that teaching practice is affected both by the teacher’s implicit knowledge and beliefs about teaching and learning and by the teacher’s explicit theoretical knowledge (on academic subjects as well as on teaching and learning), and

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that these implicit and explicit systems of knowledge and beliefs mutually influence one another, in the sense that changes in the teacher’s explicit knowledge on, for example, learning will have some degree of effect on her implicit beliefs about learning and teaching and vice versa (Woolfolk Hoy and Murphy, 2001). Furthermore, a study by Bain and colleagues (Bain, 2004) on excellence in college teaching showed that teachers’ beliefs about learning and their attitudes toward differences in student learning have a substantial impact on student learning. A strategy of implementation of learning and teaching styles in adult (or any other) education should then take into account that teaching practice is affected both by teachers’ explicit theoretical knowledge as well as their implicit knowledge on learning and teaching (Figure 1). This means that, in addition to providing teachers with explicit theoretical knowledge on learning and teaching styles, the strategy should facilitate a process of movement of the teachers’ implicit learning and teaching styles towards being explicit (the term explicitation is used in the remainder of the paper to denote this process), as illustrated in Figure 2. Such insight and understanding of their own preferences for learning and teaching will enable the teachers to consciously choose pedagogical strategies of matching and/or mismatching student learning styles and work with differentiation of teaching practice according to the students’ styles. Changing teachers’ implicit knowledge and beliefs According to Woolfolk Hoy and Murphy (2001), research on conceptual change and persuasion has shown that implicit knowledge and beliefs are resistant to change. Patrick and Pintrich (2001) argue that this research has focused on the cognitive factors involved in changing implicit beliefs and has not considered motivational or epistemological factors, and that this is the reason for the lack of success in changing implicit beliefs – their argument is supported by research showing that attitudes are resistant to change unless the emotional component of the attitude is affected as well as the cognitive component (Katzenelson, 1994). Patrick and Pintrich’s (2001, p. 118) research has shown that “. . . the process of change and restructuring of teacher beliefs is time-consuming, difficult, and long term and requires cognitive and meta-cognitive engagement (often labelled self-reflection in the teacher education literature) and

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158 Figure 2. Teachers’ learning and teaching styles – from implicit knowledge and beliefs to explicit knowledge and beliefs

persistence”. On this basis, Patrick and Pintrich suggest that a successful model of conceptual change for student teachers should include a range of cognitive, motivational and epistemological factors, as well as the connections between them, as illustrated in Figure 3. Strategy of implementation of learning and teaching styles In the present study, Patrick and Pintrich’s model of conceptual change for student teachers in a long-term teacher training perspective (Figure 3) was employed as a strategy for implementation of learning and teaching styles working with active and experienced teachers in a short-term workshop setting. The main aim of the workshops was explicitation of the teachers’ implicit beliefs about learning and teaching in terms

Figure 3. Patrick and Pintrich’s (2001) model of conceptual change for student teachers

of learning and teaching styles, thereby providing them with opportunities for changing/modifying their practice of teaching. Part one of the workshop provided the participants with a general knowledge of the theory of mental self-government (Sternberg, 1997), the concepts of learning and teaching styles, as well as the possible uses of such concepts in teaching; then proceeded self-assessment of learning styles; and finally, a more detailed explanation of the different learning and teaching styles was provided. Between parts one and two of the workshop, individual learning styles profiles for the participants were produced as well as analyses of differences and similarities in styles within the group of participants. The purpose of this was preparation for the different activities of the second part of the workshop. Part two of the workshop consisted of a number of different cognitive and meta-cognitive activities (the single activities are described in the Appendix), each connected to the different aspects of motivation and epistemological beliefs in Patrick and Pintrich’s (2001) model of conceptual change (Figure 3). Particular points of motivation incorporated into the workshop design were: (1) The choice of dividing the knowledge-providing part of the workshop into two parts separated by self-assessment of learning styles was made for two motivational purposes: . In order to engage the participants (e.g. with the self-assessment) as quickly as possible. . In order to facilitate attention to and understanding of the detailed knowledge by providing them with their own learning styles profiles to which the knowledge could be related. (2) The choice of working primarily with the participants’ personal learning styles profiles was made in order to enhance their personal interest in the process. (3) The workshop environment supported the idea that all styles were of equal value in order to lower defences and motivate participants with different styles. (4) The teachers’ sense of efficacy and control was supported by working with themselves (their own styles) and being able to choose whether or not to present results of group work, etc. Also the generally accepting environment that was supported was thought to facilitate a balance between learning something new while being satisfied with the present knowledge and beliefs. (5) To as large a degree as was found possible, the different activities were designed to accommodate different learning styles. The participants’ epistemological beliefs were engaged across the different individual and group activities as well as group and plenum discussions. The activities in their totality were aimed at making the participants’ implicit knowledge and beliefs more explicit, as well as developing these to some degree. Experimental workshops The two experimental workshops on learning, teaching and supervision styles in this study were held in 2004 and 2005. Due to the differences in teaching level and the stated purposes of the workshops, some differences were, of course, implemented in the

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individual workshops. The general outline of the workshops was, however, identical and based on the strategy of implementation presented below. The differences between the two workshops consisted of differences in general framework and organization, and primary focus of content. Evaluation In order to ascertain the effect of the learning-teaching style workshops, two self-report evaluations were conducted. Short-term evaluation A self-report evaluation of the short-term effect was conducted as a written evaluation at the end of both workshops. Participants completed a freely written evaluation of what they considered to be their personal benefits of the workshop in relation to their role as teachers with the purpose of determining: . Whether the workshops resulted in an explicitation of the teachers’ implicit beliefs about learning and teaching. . If and how the teachers themselves thought that they would change their actual teaching practice after the workshop. In the PhD workshop, 21 of 22 participants completed the evaluation, in the high school workshop all 15 participants completed the evaluation. Long-term evaluation A self-report evaluation of the long-term effect in the form of a written evaluation solicited by e-mail was conducted 14 months after the high school teacher workshop only. The participants answered seven open-ended questions devised on the basis of an analysis of the short-term evaluations given by participants in both the PhD supervisor and the high school teacher workshops with the purpose of determining: . Whether the short-term explicitation of the teachers’ implicit beliefs about learning and teaching was a genuine lasting explicitation. . If and how the teachers’ actual teaching practice had changed after the workshop according to the teachers themselves. Nine of the original 15 participants in the high school teacher workshop completed the evaluation. Results Short-term effect Across the two workshops, 26 teachers handed in their short-term evaluations – 13 from the PhD supervisor workshop and 13 from the high school teacher workshop. In the majority of evaluations (21), the teachers across the two workshops reported that the major benefit of the course was that they had become conscious of, become aware of, and/or had enhanced their awareness of their implicit beliefs and knowledge. The most common benefit reported was in terms of enhanced or new awareness of the teachers’ own preferences for thinking when learning and teaching (e.g. their learning and teaching styles) and how these styles and their expression in different

ways interplayed with and had an effect on the pedagogical classroom practice, supervision practice, and student learning. One of these areas was awareness of the role their own preferences played in connection with their interaction and communication with students in general, as expressed by high school teacher II “Some degree of enhanced awareness on the communicative situation in the classroom” and PhD supervisor XX “To be wary of the fact that working with a student with learning styles very close to one’s own learning styles profile can give rise to non-productive communication issues”. Another area of enhanced awareness was how their own learning preferences could serve as a starting point for pedagogical and didactical reflections, as stated by high school teacher BB: “. . . used as the starting point for reflections on teaching practice in relation to students and in relation to pedagogical and subject objectives.” In addition, both groups mentioned new or enhanced awareness as benefits as participants focused on the conceived value of different learning and thinking styles and the interplay between student and teacher styles – 16 participants made statements regarding this area: . Awareness of differences in the conceived value of the teachers’/supervisors’ own learning and teaching styles in different contexts and situations was reported as a benefit of the workshop by 16 participants – for example by high school teacher HH “. . . increased my knowledge on different approaches to learning and has put into words and concepts things which have been more diffuse and sporadic to me”. . Enhanced awareness of the diversity in students’ styles and how they can be valued both positively and negatively was reported as a benefit of the workshop by 13 participants, as expressed by high school teacher CC “. . . be a benefit that I now to an even higher degree than before have become aware of different students’ different reactions and performances”. . Last, enhanced awareness of the interplay between teacher and students’ styles was specifically referred to as a benefit by 15 participants – for example by PhD supervisor YY “. . . it is instructive to reflect on and become more aware of the connections between intentions and results”. In summary, there appeared to be quite a substantial effect in terms of movement of implicit beliefs and knowledge about learning and teaching to the explicit sphere of thinking, where conscious utilization and integration into the teaching practice are possible. With regard to the effect of the workshops on the actual teaching practice, 20 participants made statements to this effect in their evaluations. Of these, six participants thought that the workshop would influence their teaching practice in general – for example PhD supervisor ZZ expresses hope “. . . that I can use this on many different levels and in many different ways” and high school teacher FF states “My perspective on the interplay between teacher and students has been opened up. I have become more aware of my “style” as a teacher, but also on the students’ “style”. This I both can and will continue to work with”. Fourteen participants stated more specifically the type of change in their teaching practice they expected as a result of the workshop, with the main expected changes being accommodation of students with specific learning styles or trying out a matching approach in order to enhance the learning of “weak” students.

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In addition, in the two workshops participants expanded the field of usefulness of their newly gained knowledge and awareness in group-specific ways: . Three of the high school teachers made statements concerning new opportunities for communication and collaboration with colleagues teaching different sciences. . In the PhD supervisor group, one supervisor expanded the field of usefulness to cover M.A. student supervision, and another supervisor expanded the field to cover the role as head of a department. Long-term effect Of the 15 participants in the high school teacher workshop, nine gave a long-term evaluation 14 months after the workshop – one of these had not given the short-term evaluation in connection with the workshop. Analyses of the eight individual pairs of short-long-term evaluations showed that the explicitation of the implicit knowledge and beliefs about learning and teaching reported 14 months prior were still explicit to seven of the teachers and being used in their individual teaching practice. One teacher did not make any statements indicating explicitation of implicit beliefs or that her teaching practice had been affected by the workshop, except for trying to keep learning and teaching styles in mind when teaching – it should be noted that the teacher’s short-term evaluation had no indications of explicitation of implicit beliefs either. Analyses of the long-term evaluations individually and comparing them to the short-term evaluations showed that the implicit beliefs, which were originally reported as having become more explicit, had now expanded into new areas of awareness and/or higher degrees of awareness for five teachers. For example, teacher AA’s enhanced awareness of her own learning styles in the short-term evaluation had expanded into enhanced awareness and understanding of differences in student styles and differentiation of teaching. And teacher HH’s gained knowledge on approaches to learning in the short term evaluation had expanded into recognition of the importance of utilization of non-preferred styles in order to engage more students. A further two teachers gave statements as to the effect of the workshop on their teaching practice which indicated that the implicit beliefs that were reported as having become more explicit in the short term evaluation were still explicit. Four teachers reported that they had not had the time and energy to work systematically with learning and teaching styles in their teaching practice, due to an increased workload as a result of the high school reform. For two of these teachers, this did not mean that their teaching practice had not changed. On the contrary, both teachers reported having used more varied teaching methods and forms of work with the students as a result of the workshop. In addition, one of these teachers reported having used learning styles to define groups for group activities with the students, while the other teacher reported having become more accommodating towards students who require a specific form of teaching. The remaining four teachers reported that they had worked systematically with learning and teaching styles as an instrument for differentiation of teaching during the 14 months. These changes range from teacher FF’s “Taking into account the demands the different students might have” to teacher DD’s “Utilization of knowledge on learning and teaching styles in preparation of classes, in the teaching-learning situation, and in talks with colleagues on cross-disciplinary projects” and teacher HH’s

“... important talks with the students on the subject of learning and how to improve your learning – both at the class level and in individual mentor sessions”. In summary, the short-term explicitation of the teachers’ implicit beliefs about learning and teaching appears to be lasting and in some cases to have been further expanded. Also, the teachers’ teaching practice appears to have changed towards a greater degree of differentiation in the 14 months after the workshop.

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163 Practical implications and future research The major practical implication of the findings in any educational or business setting that wants to implement learning and teaching styles as a tool used by their teachers and/or trainers is that it is possible to do this to some degree within a relatively short time frame and at a low cost. One crucial point of discussion is the potential drawbacks of a partial implementation of learning and teaching styles in any teaching institution, as it has been done with the workshops in this study. As pointed out by one of the participating high school teachers, a partial implementation can create communication and collaboration issues between teachers who have participated in and benefited from a workshop and teachers who have not, in the sense that their professional vocabularies, their views on teaching and learning, and their teaching practice become increasingly different, thereby setting the scene for additional potential problems in team-planning of cross-disciplinary project periods. On the other hand, a partial implementation also holds positive potential for the teaching or business setting in that the resulting differences in thinking between participants and non-participants can instigate fruitful discussions of the nature of learning and teaching, which in turn can facilitate further development in both groups. Future research is needed in order to establish to which degree the present findings can be generalized across different groups of teachers and trainers and across different university cultures as well as different business settings. In addition, there are three prudent elaborations of the research into implementation of learning styles in adult or higher education in continuation of the present work. First, studies of the relationships between the learning and teaching styles of teachers are needed in order to ascertain whether the basic assumption that the learning styles of teachers are, to some degree, transferred to also being teaching styles. Second, a number of studies concerned both with the effect of a direct implementation approach at the teacher level on their teaching practice as well as the effect on student learning are prevalent, since the effect on student learning has not been included in the present work. Third, a number of studies comparing the effect of direct and indirect approaches to implementation at the student level – that is by the teachers – are also needed. Only after such studies have been completed is it possible to determine the efficiency of implementing learning styles at the teacher level in terms of pedagogical development of teachers’ practice as well as the efficiency of teachers’ subsequent approach to implementation at the student level in terms of enhancing student learning. References Bain, K. (2004), What the Best Teachers Do, Harvard University Press, Cambridge, MA. Beck, C.R. (2001), “Matching teaching strategies to learning style preferences”, The Teacher Educator, Vol. 37 No. 1, pp. 1-15.

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Brew, C.R. (2002), “Kolb’s learning style instrument: sensitive to gender”, Educational and Psychological Measurement, Vol. 62 No. 2, pp. 373-90. Busato, V.V., Prins, F.J., Elshout, J.J. and Hamaker, C. (1998), “Learning styles: a cross-sectional and longitudinal study in higher education”, British Journal of Educational Psychology, Vol. 68, pp. 427-41. Hyman, R. and Rosoff, B. (1984), “Matching learning and teaching styles: the jug and what’s in it”, Theory into Practice, Vol. 23 No. 1, pp. 35-43. Katzenelson, B. (1994), Homo Socius, Gyldendal, Copenhagen. Kolb, D.A. (1984), Experiential Learning: Experience as the Source of Learning and Development, Prentice-Hall, Englewood Cliffs, NJ. McMillin, W.L. (1999), “Learning style and teaching style interaction and the effect on psychological reactance”, PhD thesis, The Graduate College, Oklahoma State University, Oklahoma City, OK. Nielsen, T. (2005a), “Learning styles of Danish university students – do they differ according to subject of study at the start of the first academic year? – Is there a subject specific socialization effect of one year of higher education? Development of and research by means of The Danish Learning Styles Inventory (D-LSI) based on Sternberg’s theory of mental self-government”, PhD thesis, The Danish University of Education, Copenhagen. Nielsen, T. (2005b), “Spørgeskema og tolkningsmanual til selvvurdering af læringsstile (Inventory and manual for the Danish Self-Assessment Learning Styles Inventory)”, unpublished. Patrick, H. and Pintrich, P.R. (2001) in Torf, B. and Sternberg, R. (Eds), “Conceptual change in teachers’ intuitive conceptions of learning, motivational instruction: the role of motivational and epistemological beliefs”, in Torf, B. and Sternberg, R. (Eds), Understanding and Teaching the Intuitive Mind, Student and Teacher Learning, Lawrence Erlbaum, London, pp. 117-44. Severiens, S. (1997), “Gender and learning. Learning styles, ways of knowing, and patterns of reasoning”, dissertation, Universiteit van Amsterdam, Amsterdam. Sternberg, R.J. (1997), Thinking Styles, Cambridge University Press, Cambridge. Sternberg, R.J. and Grigorenko, E.L. (1995), “Styles of thinking in the school”, European Journal for High Ability, Vol. 6, pp. 201-19. Vermetten, Y.J., Vermunt, J.D. and Lodewijks, H.G. (1999), “A longitudinal perspective on learning strategies in higher education: different viewpoints towards development”, British Journal of Educational Psychology, Vol. 69, pp. 221-42. Vermunt, J.D. (1992), Leerstijlen en sturen van leerprocessen in het hoger onderwijs – Naar procesgerichte instructie in zelfstandig denken (Learning Styles and Regulation of Learning in Higher Education – Toward Process-oriented Instruction in Autonomous Thinking), Lisse, Swets & Zeitlinger, Amsterdam. Woolfolk Hoy, A. and Murphy, P.K. (2001), “Teaching educational psychology to the implicit mind”, in Torf, B. and Sternberg, R. (Eds), Understanding and Teaching the Intuitive Mind. Student and Teacher Learning, Lawrence Erlbaum, London, pp. 145-86. Zhang, L-F. (2006), “Does student-teacher thinking style match/mismatch matter in students’ achievement?”, Educational Psychology, Vol. 26 No. 3, pp. 395-409. Further reading Biggs, J. (1987), Student Approaches to Learning and Studying, Australian Council for Educational Research, Melbourne.

Bowden, J. and Marton, F. (1998), The University of Learning: Beyond Quality and Competence in Higher Education, Kogan Page Limited, London. Entwistle, N. (1988-1997), “Motivational factors in students’ approaches to learning”, in Schmeck, R.R. (Ed.), Learning Strategies and Learning Styles. Perspectives on Individual Differences, Plenum Press, New York, NY, pp. 21-52. Prosser, M. and Trigwell, K. (1999), Understanding Learning and Teaching: The Experience in Higher Education, SRHE and Open University Press, Buckingham. Valentine, K.M. (1998), “An investigation of teacher knowledge of learning styles and their possible facilitative effects on the learning process”, dissertation, University of South Florida, Tampa, FL. Appendix. Common workshop outline Day one (half day) . Summary lecture of the theoretical conception of learning and teaching styles. . Assessment of participants’ learning styles. . Questions on inventory, learning styles, etc. interlaced with research findings on learning styles of Danish university students.

Instructor’s preparation for the second day of workshop: Analysis of inventory responses to be used in talks and discussion sections. Preparation of individual learning styles profiles for participants to be used in activities. Division of participants into groups based on differences and similarities respectively to be used in the collective activities. Day two (whole day) (1) Lecture on interpretation of individual learning styles profiles. (2) Individual activity: interpretation and evaluation of own learning styles profile. Questions given for individual reflection: . What are your immediate thoughts on your learning styles profile? . Does the profile give a good description of you in learning situations in connection with your role as a teacher? . How are your learning strengths apparent in the profile? – And do they express themselves in your teaching (your teaching styles)? . How are your learning weaknesses apparent in the profile? – And do they express themselves in your teaching (your teaching styles)? (3) Pair wise (pairs formed based on differences in learning styles profiles) conversation on learning and teaching styles. Provided points of focus in the conversation: . How are your learning strengths apparent in your profile? – And do they express themselves in your teaching (your teaching styles)? . How are your learning weaknesses apparent in your profile? – And do they express themselves in your teaching (your teaching styles)? . What are the differences in your learning strengths and weaknesses? – And what are the differences in the way they are expressed in your teaching (your teaching styles)? (4) Discussion section on how to detect styles both formally (inventories, tasks, etc.) and informally (student questions, oral presentations, written assignments, conversations

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with students, etc.). This includes real-life examples of student writing, which are analyzed in plenum. Group discussion (groups formed based on similarities in learning styles profiles) on the topic “What happens in teaching when communication is not flowing the way it should?”. The group is expected to have a representative give a five minute presentation of the results of the discussion using one overhead to support the presentation. Provided points of discussion: . What characterizes the students who we find are the most difficult to understand? . What characterizes the students who are feeling over-looked, passed by, not taken seriously, run over in our teaching? . What characterizes the students with whom we experience communicative difficulties? If you look at these students in terms of learning styles and compare them to your own styles, which of the students’ styles do we then experience the most difficulty with? ( Summarize this for your presentation! Presentation of the results of the groups’ discussions by group representative (five minutes). The remaining participants are instructed to pay careful attention to what is said and to not ask questions. Plenum session in the form of a guided discussion on what caught their attention across the presentations. Points of attention are individual differences and similarities, group differences and similarities, patterns in teachers’ styles and students’ styles that give rise to communication difficulties and/or perception of resistance in students’ communicational efforts. Presentation of similarities and differences in the participants’ learning styles dependent on such factors as gender, age, teaching subjects and teaching experience. Guided discussion sections on: . Types of thinking valued individually and at different institutional levels. . Styles and strategies. . Matching and/or mismatching as pedagogical strategies. . Styles and performance. . Styles and teamwork. Individual activity: evaluation of individual benefits of the workshop as free-style writing.

Corresponding author Tine Nielsen can be contacted at: [email protected]

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Using concept mapping to measure learning quality

Concept mapping

David Hay and Ian Kinchin King’s Institute of Learning and Teaching, King’s College London, London, UK

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Abstract Purpose – This paper aims to describe a method of teaching that is based on Novak’s concept-mapping technique. Design/methodology/approach – The paper shows how concept mapping can be used to measure prior knowledge and how simple mapping exercises can promote the integration of teachers’ and students’ understandings in ways that are meaningful. Findings – The concept-mapping method facilitates quick and easy measures of student knowledge-change so that teachers can identify the parts of the curriculum that are being understood and those that are not. This is possible even among very large student groups in the 50-minute slots that are allocated to so much teaching in higher education. Research limitations/implications – Concept mapping is discussed in the wider context of student learning style. The styles literature has been criticised because it tends to encourage undue labelling of people or behaviours. The approach described here also uses “labels” to typify learning (using the terms non-learning and rote or meaningful learning to identify different qualities of change). Originality/value – The difference in this approach is that terms are attached to empirical measures of learning outcome, not to personal or psychological styles. Concept mapping makes learning visible so that the actual quality of the learning that has occurred can be seen and explored. Using concept mapping in the course of teaching means that learning is no longer a complex and intractable process, measurable only by proxy, but an observable phenomenon. Keywords Knowledge mapping, Assessment Paper type Research paper

Introduction In 1998, we came across Novak’s concept mapping method (Novak and Symington, 1982; Novak and Gowin, 1984; Novak, 1998) for the first time. We were immediately struck by its potential as a method of both research and pedagogy. Since then we have used the method with more than 1,000 people and we have inspected more than 3,000 concept maps. We have done this among school teachers and their pupils, academics and students at university and people in business. Our work has included studies in Biology, Statistics, Psychiatry, Nursing, Law, Computing Science, Education, Medical Imaging, Veterinary Science, Classics, Enterpreneurship, Geography and Education. What are otherwise complex and intractable processes of change (of different qualities) can be observed (and even measured) with the concept mapping method (Hay, 2007; Hay et al., n.d.a, b). Concept mapping is not a proxy for measurement of learning like measures of deep or surface approaches to learning and studying (Hounsell and Entwistle, 2001-2004); it is a means of exploring the real thing. Explaining how this is so is the focus of this paper. Some previously unpublished case studies of student The work reported here was funded by a grant for the research of pedagogy from the Society for Educational Studies.

Education þ Training Vol. 50 No. 2, 2008 pp. 167-182 q Emerald Group Publishing Limited 0040-0912 DOI 10.1108/00400910810862146

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learning in statistics and work to be published elsewhere on teaching in academic development are included to exemplify our arguments. In the last two decades, models of learning style have (probably) contributed more to the literature of higher education teaching than any other (general) approach. Nicholls(2002), Prosser and Trigwell(1999), and Ramsden(1992), for example devote considerable discussion to issues of learning style in higher education. Moreover, most university teachers acknowledge issues of student styles and approach more frequently than any other general issue (personal experience). Recently, however, the general learning styles literature has been criticised for displaying a lack of unity in the field (e.g. Coffield et al., 2004a, b; Sternberg, 2001). Ashwin(2005) for example acknowledges some of the more acute failings of styles (or perhaps more accurately, the ways in which styles have been used), in ELSIN Newsletter (Winter 2005/2006). He also explains how appropriate responses may emerge for the community of styles researchers. But he says that this is “not the time to search for new labels, but to look at what the research has to tell us so far . . .” (Ashwin, 2005). We agree and if anything, go further. The literature of higher education is littered with ill defined models that have emerged without appropriate theory development; nor are many of them grounded in empirical data. In this paper we aim to show how concept mapping can be used to research student learning in the normal course of teaching so that future theory can emerge out of direct observation. Concept mapping and student prior knowledge Concept mapping is one of a broad family of graphic organising tools that includes mind-mapping (Buzan and Buzan, 2000), spider diagramming and other related approaches. Yet Novak’s method of concept mapping (Novak, 1998) has some very specific rules that set it apart from other techniques and facilitates the measurement of learning quality. First a concept map is a hierarchical map of concept labels. Big and inclusive ideas are placed at the top, exemplary or subordinate ones below. Second, concepts are linked with arrows and the arrows are labelled to explain the nature of the association. Thus, concept maps comprise any number of propositional statements each of them made up of paired and linked concepts. Each proposition is a statement of understanding and the validity of each assertion is laid bare. This is shown in Figure 1. The concept mapping method can be taught in about 20 minutes and most people will find that another 30 to 40 minutes is sufficient to make satisfactory maps of most topics. Figure 2 shows four students’ prior knowledge maps of statistics. These concept maps were obtained in a 50 minute lecture and are just four examples from a total of 227 that were made simultaneously. The maps in Figure 2 show just how different, students can be. This group of four students used spokes (B1, D1), chains (C1) and network (A1) structures (Kinchin et al., 2000) to organise their prior-knowledge and the quality of propositions comprising their maps varied from the rich (A1) to the trivial (B1 & D1) or merely procedural (C1). Without exception individual prior-knowledge structures (among all 227 students) were unique personal constructions. In Figure 2, the difference in students’ prior-knowledge is shown by presenting the maps themselves. This is fine when cases studies are described individually. To extract broader generalities, however, (or to make quantitative comparison between maps) then rules of analysis are needed. Novak (1998) provides a marking scheme that

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Figure 1. A concept map structure

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Figure 2. Student’s prior knowledge of statistics

can be used to give grades to concept maps. These reward link number, structural complexity and hierarchy. One mark is given for each link (L), five marks for each level of hierarchy (H) and ten for every crosslink (C) (Novak, 1998). Using this system, the maps in Figure 2 can be graded as follows: A1 ¼ 33; B1 ¼ 18; C1 ¼ 57; D1 ¼ 22. Kinchin et al. (2000), however, have criticised this approach on the grounds that it reduces rich pictures to number, and that these numbers are often misleading. The fact that map C1 scores considerably more than A1 (above) illustrates the point. Scoring like this can obscure structural differences (one map is cognitive [A1]; the other is procedural [C1] and neglects real differences in grasp of the topic). Without more careful attempts to examine the validity of the propositions that are declared in each map and to assess the quality of each link then high scores in the quantitative system can reward complexity even where it is nonsense. To this end, Hay et al. (2008b) have developed scoring methods that are based on subjective measures of concept richness, linkage quality, structure and the complexity of propositions. Such evaluative criteria can easily be agreed a priori (like other marking and assessment schemes) and formalised through the use of Likert scales, for example. Kinchin et al. (2000) also provide a very quick qualitative method based on gross cognitive structure. This cannot be used to separate maps by their quality but it provides rich insights into organisation and cognitive structure (Kinchin et al., 2000; Hay and Kinchin, 2006). Learning as change Once prior-knowledge has been assessed through concept mapping then mapping the same topic later facilitates visualisation of any change that has occurred. Figure 3 shows concept maps of statistics made after learning by the same four students in our case study. From their different starting points (Figure 2), all of them have gone on to construct different understandings of the topic. Like their prior-knowledge structures, each construction of the topic is different. The quality of change is also unique to each case. Some of the changes are structural and can be used to exemplify the spoke chain and networks models that we have already seen (sensu, Kinchin et al., 2000). Thus, for example the change C1 to C2 is notable as a change from a chain (C1) to a network (C2). The differences A1 to A2 and B1 to B2 are developmental, however, not structural (case A shows increased network development and case B enlargement of a pre-existing spoke structure). The change in case D (D1 to D2), however is different. The second structure (D2) is not a map at all, but a list of unrelated concept labels. This student has acquired new ideas but has been unable to make sense of them. Their first map was trivial (D1) but their second probably indicates further confusion. In this case concept mapping has helped to show that a particular student who has acquired new information about a subject has done so without grasping meaning. Their new knowledge comprises sets of concept labels (representing elements of the taught programme), but no grasp of understanding. Concept mapping can also be used so that post-course knowledge can be compared with what was known before (compare these structures with those in Figure 2). Analysis of structure, notwithstanding, the changes in these students maps offer direct insights into the ways that they learnt (or did not). Since the maps were made of the same topic, before and after teaching, some simple criteria can be applied to measure the quality of the change that has taken place. A framework for this has been

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Figure 3. Student’s knowledge of statistics at the end of learning

developed by Hay (2007) after Jarvis (1992) and Novak (1998). The criteria were as follows: . Non-learning. Was defined by an absence of cognitive change. Non-learning was therefore measured by the lack of new concepts in the second map and by an absence of new links in the extant prior knowledge structure. . Rote learning. Was defined in two ways. First by the addition of new knowledge. Second by absence of links between the newly acquired concepts and those parts of the prior knowledge repeated in the second map. . Meaningful learning. Was defined by a non-trivial change in the knowledge structure. Thus evidence of meaningful learning comprised the emergence of new links in parts of the prior knowledge structure developed in the course of learning and/or the meaningful linkage of new concepts to parts of the pre-existing understanding. Using these criteria, the change for student A (A1 to A2) is meaningful learning (because new knowledge has been combined with the prior-knowledge structure). Student B hardly changed at all (B1 to B2) and shows Non-Learning. Case C (C1 to C2) is a mix of rote and Meaningful Learning and D (D1 to D2) is Rote Learning (new ideas have been acquired, but without liking statement to explain understanding, they have no meaning). Making change visible These examples show how quality of student learning can be made visible and assessed using concept mapping. Nevertheless, the criteria explained here (for assessing the quality of change in successive maps of the same topic) are also broadly similar (in epistemology) with the distinction between deep and surface learning (see Sa¨ljo¨, 1975: Marton and Sa¨ljo¨, 1976, 1984; Marton, 1986). This means that the approach can be included among many other models of learning (some of which have been the antecedents of various learning styles approaches). Nevertheless, the concept mapping approach introduces some important differences. In particular, concept mapping makes the actual process of change visible in the rich detail of case studies. It does not use proxies for learning approach (Hounsell and Entwistle, 2001-2004) nor phenomenographic descriptions of students’ conceptions of learning (e.g. Marton and Sa¨ljo¨, 1976); it gives “snap-shots” of the real thing. It may be argued that using labels for learning quality (e.g. non-learning, rote learning and meaningful learning) invites many of the same criticisms that have come to haunt the learning styles approach more generally. Ashwin (2005), for example, suggest that it is the labels of styles that have been its undoing because it has led to undue casting of types into apparently immutable styles. But labels like rote or meaningful learning are not contentious (nor misleading) when they are attached to discrete measures of change (rather than to people). They are then commentaries on the actual events of learning and not attempts to describe personal or psychological approaches. Furthermore, concept mapping suggests that despite discrete classification of learning outcomes, in reality all learning is located in a single continuum. Thus learning is more rote or more meaningful in one case than another. This is confirmed by the fact that concept mapping makes each measure of individual student change a unique personal study. The message is reinforced because the theory of meaningful

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learning (Novak, 1998 (after Ausubel (2000))) (that underpins concept mapping) places all learning in a single continuum (from rote to meaningful learning). Thus, Novak (1998) states that meaningful learning has the following traits: . Relevant prior knowledge. That is, the learner must know some information that relates to the new information to be learned in some nontrivial way. . Meaningful material. That is, the knowledge to be learned must be relevant to other knowledge and must contain significant concepts and propositions. . The learner must choose to learn meaningfully. That is, the learner must consciously and deliberately choose to relate new knowledge to knowledge the learner already knows in some nontrivial way (Novak, 1998, p. 19). Similarly, Ausubel (before Novak) writes that: Meaningful reception learning primarily involves the acquisition of new meanings from presented learning material. It requires both a meaningful learning set and the presentation of potentially meaningful material to the learner. The latter condition, in turn, presupposes (1) that the learning material itself can be nonarbitrarily (plausible, sensibly, and nonrandomly) and nonverbatimly related to any appropriate and relevant cognitive structure (i.e. possesses “logical” meaning) and (2) that the particular learners cognitive structure contains relevant anchoring ideas to which the new material can be related. The interaction between potentially new meanings and relevant ideas in the learner’s cognitive structure gives rise to actual or psychological meanings. Because each learner’s cognitive structure is unique, all acquired new meanings are perforce themselves unique (Ausubel, 2000, p. 1 (revised from a monograph published in 1963)).

These definitions are important because they make explicit statements about what learning (and teaching) is. But they also remind us that meaningful learning is not an absolute but a trait of learning that is measured by degree. Rote and meaningful learning are not dichotomous and mutually exclusive: they are different parts of the same continuum. They can even occur, simultaneously. Some of our earlier work has already shown that some parts of a student’s cognitive structure can remain unchanged in the course of learning (Non-learning), while other zones are changed superficially (Rote learning) and still others are changed through purposeful and deliberate integration of new knowledge among the prior-knowledge structure (Meaningful learning). In the end, however, it is the fact that these things can be made visible, (to teachers and to students), that sets the concept mapping method apart from other approaches. Prior-knowledge and the quality of change As the theory of meaningful learning (above), suggests, concept mapping also shows that student prior-knowledge quality is a good predictor of knowledge at the end of a course (of learning). Prior-knowledge is also a significant determinant of the quality of change (Hay et al., 2008a, b). Simply, students who know more to begin with know more at the end of a course, not just because they had a stronger start, but also because this made them better able to make sense of the things they were taught. Within this study the quality of learning for students was influenced by prior-knowledge. These data show how prior-knowledge is a key-issue for university lecturers. Understanding what students know to begin with is necessary if lecturers want to teach meaningfully (Ausubel, 2000; Novak, 1998). This requires more than setting

common entrance requirements to university courses; as the data here show, the richness of student constructions can vary from the “expert” to the “trivial” in a single cohort. To acknowledge this is important, but identifying student prior-knowledge is also a means of identifying those most at risk of failure. Concept mapping can be used in very large classes just as easily as it is in small groups and it facilitates the declaration of prior-knowledge in ways that are rarely achieved outside the one-to-one student-teacher encounter. Even if lecturers feel that other commitments prohibit all but the most cursory glance at their students’ maps this is still a significant acknowledgement of the importance of prior-knowledge. The process of concept mapping will help to bring their students face-to-face with their own learning needs and will reinforce the message that ultimately university learning is about reflection, evaluation and synthesis in order to create new and personal meaning (Hay et al., n.d.). Making teachers’ knowledge declarative Thus far we have been talking about concept mapping of student knowledge, but concept mapping also has an important role to play in making “expert” constructs available to students. The “traditional” lecture is an “expert” narrative but it does not always explain the meaning out of which lecturers have constructed their teaching. Most lecturers plan their stories from beginning to end and reason that if they explain the topic carefully enough then what they have to say will be understood by their students. More often than not, however, their messages can be grasped only by those who have already understood them. The problem is compounded by the fact that different “experts” will usually teach different parts of the same university course. Experts have different fields of expertise and as a consequence the underlying mental models out of which they each develop their teaching are different (see Kinchin et al., 2005 for a case study of this issue in undergraduate Microbiology). The authors of this paper are part of an eight-person teaching-team on a single programme of academic development. The main focus of the course is to teach university lecturers about the theory and practice of teaching. Nevertheless, Lygo-Baker et al. (n.d.), have used concept mapping to show that among the eight of us, there are as many different conceptions of the purpose of teaching as there are people. Similarly Hay and Kinchin (2007) describe how four different teachers on the same third year course in Cardiology describe their own understanding of heart failure differently. One emphasises surgical intervention, one physiology, another talks exclusively about drug design, and the last, mentions only diet and exercise. These are a natural consequence of their respective areas of expertise but there is, in fact, so little overlap between their models (Hay and Kinchin, 2007) that it is difficult to see how their students can learn anything other than four different and unrelated versions of the same topic. This can only promote rote learning.. Without an over-arching grasp of the topic the novice (student) does not have enough structure to be able to locate each different narrative in frameworks that enable meaning-making (Kinchin and Hay, 2007; Kinchin et al., n.d.). Concept mapping has some important roles to play here too. First it can be used to help teachers declare the networks of knowledge out of which their narrative sequences are constructed. This is useful to students because it shows them that a lecture is not a correct sequence of related ideas to be learnt by rote, but merely one (of many different and competing) explanations for a given topic (Kinchin and Hay, 2007). Second it can facilitate the integration of different experts’ views in the process of curriculum design

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and helps students to understand where different “expert views” meet in the broader framework of a particular discipline. As shown in Figure 4 both these uses have a role in facilitating an on-going dialogue between students and teachers in the course of university teaching.

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Expert models Students can also be helped to make sense out of the new material they are taught using simple concept mapping exercises. In our own teaching we have developed a variety of different approaches to these ends (although all of them depend on the principle that student prior-knowledge should be declared before teaching). One approach is to use “expert” models as frameworks for assessing the conceptions of novice students. This can be done by giving students opportunities to compare their own personal constructions with those of their lecturers (e.g. Lygo-Baker et al., n.d.) or with purpose-built models adapted from the literature (e.g. Kinchin et al., 2008). In this approach there is a risk that students will be tempted to abandon their own models for

Figure 4. A model of university teaching

those that are already labelled “expert”, but the declaration of student prior-knowledge before-hand, helps to minimise this. It can be prevented altogether by asking students to integrate the concept labels that have been declared in their teachers’ maps with their own prior knowledge structures. New labels that are added superficially will be indicative of rote learning but meaningful learning will also be shown where the old and new ideas are integrated to crate new meaning (Hay, 2007). The exercise is useful to students because it helps them to engage in the process of meaning-making. It is also valuable to teachers since it shows those parts of teaching that make sense to students and those that do not. Figures 5 and 6 are a case study of this method in teaching for academic development at university. These case studies are adapted from real examples in Hay et al. (n.d.). They show how one student [L] was able to integrate the new material (bold concept boxes) through Meaningful learning while another [M], added new material, merely by Rote. The data emphasise the importance of student prior-knowledge and issues of “fit” between the prior-knowledge structures of students and their teachers. Exposing these outcomes of learning gives teachers considerable purchase on the process of change among their students and helps them to identify parts of the curriculum that are neglected. In the more general literature on concept mapping student maps are commonly scrutinised to assess miss-conceptions and to reward the gradual convergence on the “expert” view. This is true of the schools literature on concept mapping in particular (e.g. Daley, 2002a, b; Novak and Gowin, 1984; Novak and Mussonda, 1991; Novak, 1998), but it is not the focus of the exercise described here. This is because we think that learning at university is more complex than mastery of the “given”. Teaching at university spans a basic introduction to academic and professional specialism (in first year undergraduate studies), right through to the supervision of research activities (that can lead to the creation of new knowledge in society). Concept mapping has a role to play at all levels of university teaching and learning but it is important that university teachers learn to use it in ways that acknowledge its potential as an intellectual “trading-zone” (e.g. Kinchin and Hay, 2007), not as just another summative assessment task. Conclusions This paper has described four basic uses of the concept mapping method in higher education. These are: (1) The identification of prior-knowledge (and prior-knowledge structure) among students. (2) The presentation of new material in ways that facilitate meaningful learning. (3) The measurement of learning quality. (4) The documentation of knowledge-change to show integration of student prior-knowledge and “expert” teaching. We have shown that concept mapping is quick and easy to use and has utility even among very large student groups. Most important, however it is a means of achieving goals widely acknowledged but rarely embodied in practice. Concept mapping arises from a set of basic principles that include the following issues:

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Figure 5. Using concept mapping in teaching

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Figure 6. Using concept mapping in teaching

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Learning is change and personal meaning-making is the process by which change is acquired. Prior-knowledge and its structure are significant determinants of learning quality. Meaningful teaching comprises measurement of student prior-knowledge (so that new material can be introduced meaningfully). Teachers should strive to disclose their own personal constructions in ways that are declarative (but also acknowledge the fact that other people see things differently). Meaningful learning can be facilitated through teaching but ultimately the making of new meaning is a personal endeavour.

Our work with new university lecturers has highlighted some of the issues they encounter when trying to use learning styles to inform teaching practices. When faced with the great variety of learning styles inventories (e.g. Cassidy, 2004), lecturers are confused. Widely publicised criticism of the learning styles literature (e.g. Coffield, 2004a; b) add to their uncertainty. Additionally, even when colleagues are intuitively drawn to the learning styles literature and have obtained data on their students, they are unsure how to proceed to maximize gains from their appreciation of student diversity. This is particularly acute when lecturers are faced with several hundred students in a lecture theatre for only a few weeks. In the absence of a clear pedagogical approach that is practical in the university context, such information is viewed as disempowering for lecturers who often lack the influence to change teaching arrangements and assessment procedures accordingly. Like the literature on learning styles, concept mapping emphasises issues of individual student difference. But it has two great advantages over the styles approach. First it attaches labels (or measures) of learning to concrete phenomena. It does not label people as ROTE or Meaningful but it allows the quality of the learning that individuals achieve to be seen for what it is. Second, it is a practical method for teaching. The learning styles literature has never found a genuine repost for the criticism that it fails to tell teachers how to teach different students differently despite suggesting that this is necessary. Concept mapping closes this theory – practice gap because it a means of facilitating personal meaning-making. “Learning is personal” says Jarvis (2006, p. 22.) and concept mapping is one way that university teachers can ensure that it is. References Ashwin, A. (2005), “Thoughts on the Coffiield report”, ELSIN Newsletter, available at: www. elsinnet.com/2005ELSINnewsletter.pdf (accessed 17 July 2007). Ausubel, D.P. (2000), The Acquisition and Retention of Knowledge: A Cognitive View, Kluwer Academic Publishers, Dordrecht. Buzan, T. and Buzan, B. (2000), The Mind Map Book, BBC Worldwide Ltd, London. Cassidy, S. (2004), “Learning styles: an overview of theories, models and measures”, Educational Psychology, Vol. 24 No. 4, pp. 419-44. Coffield, F.J., Moseley, D.V., Hall, E. and Ecclestone, K. (2004a), Should We Be Using Learning Styles? What Research Has to Say to Practice, Learning and Skills Research Centre, London/University of Newcastle upon Tyne, Newcastle.

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