Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach

In learning management systems (LMSs), teachers have more difficulties to notice and know how individual students behave and learn in a course, compared to face-to-face education. Enabling teachers to know their students’ learning styles and making students aware of their own learning styles increases teachers’ and students’ understanding about the students’ learning process, allows teachers to provide better support for their students, and has therefore high potential to enhance teaching and learning. This paper proposes an automatic approach for identifying students’ learning styles in LMSs as well as a tool that supports teachers in applying this approach. The approach is based on inferring students’ learning styles from their behaviour in an online course and was developed for LMSs in general. It has been evaluated by a study with 127 students, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. DeLeS, the tool which implements this approach, can be used by teachers to identify students’ learning styles and therefore to support students by considering their individual learning styles.

[1]  M. D. Roblyer,et al.  Design and Use of a Rubric to Assess and Encourage Interactive Qualities in Distance Courses , 2003 .

[2]  Sabine Graf,et al.  Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles , 2007 .

[3]  Gustaf Neumann,et al.  The Learn@WU Learning Environment , 2003, Wirtschaftsinformatik.

[4]  W. Hall,et al.  Incorporating learning styles in hypermedia environment: Empirical evaluation , 2003 .

[5]  Maria Platsidou,et al.  Validity and Reliability Issues of Two Learning Style Inventories in a Greek Sample: Kolb's Learning Style Inventory and Felder & Soloman's Index of Learning Styles , 2009 .

[6]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[7]  G. Pask STYLES AND STRATEGIES OF LEARNING , 1976 .

[8]  Alfred P. Rovai,et al.  On-Line Course Effectiveness: An Analysis of Student Interactions and Perceptions of Learning , 2007 .

[9]  Kinshuk,et al.  An Approach for Detecting Learning Styles in Learning Management Systems , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

[10]  雅文 大喜,et al.  WebCTを使用した講義評価に関連する要因 看護学生に対する「社会福祉コース」履修者のデータ分析から , 2003 .

[11]  Yong Se Kim,et al.  Learning Styles Diagnosis Based on User Interface Behaviors for the Customization of Learning Interfaces in an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[12]  D. Kolb Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[15]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[16]  Margaret Honey,et al.  A manual of learning styles , 1986 .

[17]  Analía Amandi,et al.  Evaluating Bayesian networks' precision for detecting students' learning styles , 2007, Comput. Educ..

[18]  F. Coffield Learning styles and pedagogy in post-16 learning: a systematic and critical review , 2004 .

[19]  Lakhmi C. Jain,et al.  Introduction to Bayesian Networks , 2008 .