Article

Automatic student modelling for detecting learning style preferences in learning management systems

School of Computing and Information Systems, Athabasca University, Athabasca, Canada
07/2009;

ABSTRACT

Providing adaptivity based on learning styles can support learners and make learning easier for them. However, for providing proper adaptivity, the learning styles of learners need to be known first. While most systems, which consider learning styles, use questionnaires in order to identify learning styles, we propose an automatic student modelling approach, which analyses the actual behaviour and actions of students during they are learning in an online course in order to infer students' learning styles. Such an automatic approach has the advantage that students do not have any additional effort for providing information about their learning styles. Additionally, an automatic approach can be more accurate by excluding extraordinary behaviour of students and adapting in the case that the learning styles changed over time. In this paper, we present an automatic student modelling approach for learning management system, which aims at identifying learning style preferences within the four dimensions of the Felder-Silverman learning style model (FSLSM). The approach is based on patterns derived from literature and a simple rule-based method for calculating learning styles from the students' behaviour. The proposed approach is evaluated by a study with 75 students, comparing the results of the learning style questionnaire with the results obtained by the proposed automatic student modelling approach. As a result, the approach is appropriate for identifying all learning style preferences within the active/reflective dimension of FSLSM and some learning style preferences within the sensing/intuitive and visual/verbal dimension. For the sequential/global dimension, results of learning style preferences show only moderate precision.

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    • "Berdasarkan banyak hasil penelitian, FSLSM merupakan learning style yang paling sesuai untuk diaplikasikan dalam elearning atau pembelajaran online lainnya. Secara jelas, teori FSLSM membedakan preferensi atau karakteristik siswa dalam menerima dan mengolah informasi ke dalam empat dimensi[10]. "
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    • "However, their approaches have varied. The methods used include datadriven approaches such as Bayesian networks [6], [16], [17], decision trees [18], hidden Markov models [18], clustering algorithms [19], as well as simple rule-based approaches [17], [20]. Most LMSs or similar systems are concerned with presenting the same course materials to numerous learners. "
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