The relationship between learning styles and cognitive traits – Getting additional information for improving student modelling
Introduction
Student models (for example, see Brusilovsky, 1994) are essential to any adaptive virtual learning environments. These models contain information about learners such as personal data, domain competence, learning style and cognitive traits, and use this information to adapt to the learners’ needs. An important task for such adaptive environments is to build a robust student model in order to be able to provide adaptivity in an appropriate way, but filling the student model with proper information about the learner is quite challenging.
The simplest approach to construct a student model is to ask a student for relevant data. However, this approach is not suitable for identifying accurate information for a number of components of a student model, such as cognitive traits, domain competence, and preferred learning styles. For example, the estimation of domain competence is subjective. To determine cognitive traits and learning styles, comprehensive tests or questionnaire-based surveys are the ordinary means used but these are time consuming and hardly definitive. An alternative approach to collect the information pertinent to a student model is to track the student’s behaviour and responses and then make inferences about general domain competence, cognitive traits, and learning styles. The challenge of this approach is to identify and collect sufficient information to make reliable and useful inferences. To support the detection process of required information, it is beneficial to find mechanisms that use whatever information about the learner is already available to obtain as much reliable information as possible to build a more robust student model.
The aim of this paper is to demonstrate the relationship between the learning style and the cognitive traits of a learner. The identified relationship provides additional information which can be used to improve the detection process of both, the learning style and the cognitive traits, in an adaptive virtual learning environment.
To exemplify this relationship, we investigate the interaction of working memory capacity, one cognitive trait included in the cognitive trait model (Lin et al., 2003), with Felder–Silverman learning style model (Felder & Silverman, 1988). Both models as well as their possible implementation in adaptive virtual learning environments are described in the following section in more detail. In Section 3, we present the mapping between the Felder–Silverman learning style model and working memory capacity. This mapping is derived from links between learning styles, cognitive styles, and working memory capacity which are based on studies from the literature. Section 4 points out the results as well as the benefits of the identified relationship. Section 5 then concludes the paper.
Section snippets
Description of the learning style model and the cognitive trait model
In this section, two models – the Felder–Silverman learning style model (FSLSM) and the cognitive trait model (CTM) – are explained to provide background information for the current investigation. While several learning style theories exist in the literature, for example, the learning style model by Kolb, 1984, Honey and Mumford, 1982, FSLSM seems to be most appropriate for the use in educational systems. Most other learning style models classify learners as belonging to a few groups, whereas
Mapping of the Felder–Silverman learning style model to the cognitive trait model
In this section the relationship between one cognitive trait, namely working memory capacity, and each of the dimensions of the Felder–Silverman learning style model (FSLSM) is described. This interaction can be used to support the identification process of both, learning styles and cognitive traits.
In our investigations we also incorporated cognitive styles. One of the most extensively studied cognitive styles with wide application to educational problems is the
Results and benefits of the relationship between learning styles and cognitive traits
The results of current investigations show that relationships exist between working memory capacity and the four dimensions of Felder–Silverman learning style model. Table 1 summarises the discussed relationships. It should be pointed out that these relationships show tendencies. For example, current investigation indicates that most of the learners with high working memory capacity tend to have a reflective learning style and vice versa.
There are two benefits of the identified relationship
Conclusion and future work
The aim of this paper is to identify interactions between learning styles and cognitive traits. Considering the learning style, we based our investigations on the Felder–Silverman learning style model. As an example for cognitive traits, working memory capacity was applied. As a result, interactions between the dimensions of the learning style model and working memory capacity have been identified. Learners with low working memory capacity tend to prefer an active, sensing, visual, and global
Acknowledgements
This research has been partly funded by the Austrian Federal Ministry for Education, Science, and Culture, and the European Social Fund (ESF) under Grant 31.963/46-VII/9/2002 and partly by Online Learning Systems Ltd. in conjunction with the New Zealand Foundation for Research, Science & Technology.
Sabine Graf is graduate researcher at Women’s Postgraduate College for Internet Technologies at Vienna University of Technology, Austria. She is also researcher at Advanced Learning Technology Research Centre of Massey University, New Zealand. Her research interests include adaptivity in web-based educational systems, student modelling, and artificial intelligence.
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Sabine Graf is graduate researcher at Women’s Postgraduate College for Internet Technologies at Vienna University of Technology, Austria. She is also researcher at Advanced Learning Technology Research Centre of Massey University, New Zealand. Her research interests include adaptivity in web-based educational systems, student modelling, and artificial intelligence.
Taiyu Lin is Graduate Researcher at the Advanced Learning Technology Research Centre of Massey University, New Zealand. He is also Assistant Editor of the Journal of Educational Technology & Society (ISSN 1436-4522). He is also Secretary of New Zealand Chapter of ACM. His research interests include exploratory learning, human cognition, and learner profiling.
Kinshuk joined Athabasca University in August 2006 as the Professor and Director of School of Computing and Information Systems. Before moving to Canada, Kinshuk worked at German National Research Centre for Information Technology as Postgraduate Fellow, and at Massey University, New Zealand as Associate Professor of Information Systems and Director of Advanced Learning Technology Research Centre. He also holds Honorary Senior E-Learning Consultant position with Online Learning Systems Ltd., New Zealand, and Docent position with University of Joensuu, Finland. He has been involved in large-scale research projects for exploration based adaptive educational environments and has published extensively in international refereed journals, conferences and book chapters. He is Chair of IEEE Technical Committee on Learning Technology and International Forum of Educational Technology & Society. He is also editor of the SSCI indexed Journal of Educational Technology & Society (ISSN 1436-4522).