Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis

Xiao, Li Ling and Abdul Rahman, Siti Soraya (2017) Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis. Current Science, 113 (11). pp. 2090-2096. ISSN 0011-3891, DOI

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Past research has proposed various approaches to automatically detect students' learning styles to address problems associated with traditional research methods (i.e. questionnaire). However, results obtained through traditional research methods have issues in terms of accuracy and precision which need to be addressed. In general, the existing automatic detection approaches are only able to provide satisfactory results for specific learning style models and/or dimensions, or even only work for certain learning management systems. The aim of this study is to propose an automatic detection of learning styles from the analysis of students' learning behaviour by constructing a mathematical model. This study specifically explores the relationship between students' learning behaviour and their learning styles. To investigate this relationship, a pilot experiment was conducted with 33 students. The students used Moodle platform, a learning management system, as supplementary online learning material for Java programing. The students' learning behaviour was tracked and recorded. Thirty students' data (i.e. their learning behaviour and learning styles; measured using the Index of Learning Styles (ILS) instrument) were analysed using the proposed correlation analysis to identify the relationship. The remaining three students' learning behaviour data were used to predict their learning styles. The findings are discussed with regard to accuracy of automatic detection of learning styles using the ILS instrument.

Item Type: Article
Uncontrolled Keywords: Automatic learning style assessment; Learning behaviour pattern; Student modelling
Subjects: L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 21 Oct 2019 03:34
Last Modified: 21 Oct 2019 03:34

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