Academic journal article Educational Technology & Society

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

Academic journal article Educational Technology & Society

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

Article excerpt

Introduction

In traditional learning, teachers can easily get an insight into how their students work and learn. However, in online learning, especially when using systems like learning management systems (LMSs), it is more difficult for teachers to see how individual students behave and learn in the system. LMSs such as Moodle (2009), Sakai (2009), and WebCT (2009) are commonly and successfully used in e-learning. They aim at supporting teachers in creating and managing online courses. However, with respect to providing teachers with information about their students, they mainly show how the overall class is using a course rather than focusing on individual students.

This paper focuses on supporting teachers in identifying their students' learning styles in LMSs. Learning styles can be seen as "a description of the attitudes and behaviours which determine an individual's preferred way of learning" (Honey & Mumford, 1992, p. 1). Many learning style models exist in literature, such as the learning style model by Kolb (1984), Honey and Mumford (1982), Pask (1976), and Felder and Silverman (1988). While there are still many open issues with respect to learning styles, the learning style models agree that learners have different ways in which they prefer to learn. Furthermore, many educational theorists and researchers consider learning styles as an important factor in the learning process and agree that incorporating them in education has potential to facilitate learning for students.

Knowing students' learning styles can help in many ways to enhance learning and teaching. First, teachers can benefit by getting information about how their students are used to learn, which provides them with a deeper understanding and might help when explaining or preparing learning material. Furthermore, making students aware of their learning styles and showing them their individual strengths and weaknesses can help students to understand why learning is sometimes difficult for them and is the basis for developing their weaknesses. In addition, students can be supported by matching the teaching style with their learning style. Providing students with learning material and activities that fit their preferred ways of learning can make learning easier for them. This matching hypothesis is supported by many educational theories, as stated and described by Coffield, Moseley, Hall, and Ecclestone (2004). Examples for studies which demonstrated supportive results of this hypothesis include those by Bajraktarevic, Hall, and Fullick (2003) and Graf and Kinshuk (2007).

For considering learning styles in education, the students' learning styles need to be known first. Brusilovsky (1996) distinguished between two different ways of student modelling: collaborative and automatic. In the collaborative approach, the learners provide explicit feedback which can be used to build and update a student model, such as filling out a learning style questionnaire. In the automatic approach, the process of building and updating the student model is done automatically based on the behaviour and actions of learners while they are using the system for learning. The automatic approach is direct and free from the problem of inaccurate self-conceptions of students. Moreover, it allows students to focus only on learning rather than additionally providing explicit feedback about their preferences. In contrast to learning style questionnaires, an automatic approach can also be more accurate and less error-prone since it analyses data from a specific time span rather than data which are gathered at one specific point of time.

In this paper, we propose an automatic student modelling approach for identifying learning styles in LMSs as well as a tool that implements this approach. Both, the approach and the tool are developed in a generic way and are therefore applicable for LMSs in general. For the theoretical basis regarding learning styles, a well-known and often used learning style model, especially in technology enhanced learning, has been selected, namely the Felder-Silverman learning styles model (FSLSM) (Felder & Silverman, 1988). …

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