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


Affiliations
1 Department of Artificial Intelligence, Universiti of Malaya, Malaysia
 

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.

Keywords

Automatic Learning Style Assessment, Learning Behaviour Pattern, Student Modelling.
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  • Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis

Abstract Views: 385  |  PDF Views: 113

Authors

Li Ling Xiao
Department of Artificial Intelligence, Universiti of Malaya, Malaysia
Siti Soraya Binti Abdul Rahman
Department of Artificial Intelligence, Universiti of Malaya, Malaysia

Abstract


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.

Keywords


Automatic Learning Style Assessment, Learning Behaviour Pattern, Student Modelling.

References





DOI: https://doi.org/10.18520/cs%2Fv113%2Fi11%2F2090-2096