Article: Integrating data mining techniques for analysing implicit user behaviours in online courses Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.6 pp.21 - 48 Abstract: The rapid expansion of online education has made the analysis of users' implicit behaviours - viewed through the lens of nonlinear and complex data - a crucial avenue for enhancing educational effectiveness. To address this, we introduce a random forest-fuzzy comprehensive evaluation (RF-FCE) method embedded within a clustering framework. Leveraging multiple clustering techniques, we first identify distinct category-specific influence patterns across different courses. Subsequently, we integrate fuzzy comprehensive evaluation with machine learning to analyse implicit behavioural data, examining both the intrinsic factors that affect course outcomes and the complex interactions between these factors and course quality. Our findings reveal significant variations in user engagement and learning outcomes across courses of differing quality, with these variations exerting a substantial influence on learning behaviours. In summary, this study offers a structured and robust analytical approach for examining implicit user behaviours in online education, demonstrating both methodological innovation and practical utility for improving course design and delivery. Inderscience Publishers - linking academia, business and industry through research

Title: Integrating data mining techniques for analysing implicit user behaviours in online courses

Authors: Guizhi Li; Dake Jiang; Dawei Kong

Addresses: School of Economics and Management, Yingkou Institute of Technology, Yingkou, 115004, China ' Public Security General Teaching Department, Criminal Investigation Police University of China, Shenyang, 110854, China ' School of Economics and Management, Yingkou Institute of Technology, Yingkou, 115004, China

Abstract: The rapid expansion of online education has made the analysis of users' implicit behaviours - viewed through the lens of nonlinear and complex data - a crucial avenue for enhancing educational effectiveness. To address this, we introduce a random forest-fuzzy comprehensive evaluation (RF-FCE) method embedded within a clustering framework. Leveraging multiple clustering techniques, we first identify distinct category-specific influence patterns across different courses. Subsequently, we integrate fuzzy comprehensive evaluation with machine learning to analyse implicit behavioural data, examining both the intrinsic factors that affect course outcomes and the complex interactions between these factors and course quality. Our findings reveal significant variations in user engagement and learning outcomes across courses of differing quality, with these variations exerting a substantial influence on learning behaviours. In summary, this study offers a structured and robust analytical approach for examining implicit user behaviours in online education, demonstrating both methodological innovation and practical utility for improving course design and delivery.

Keywords: online course; data mining technology; implicit behaviour; cluster analysis; online education; course quality evaluation; behaviour analysis.

DOI: 10.1504/IJDMB.2026.153892

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.6, pp.21 - 48

Received: 28 Oct 2025
Accepted: 15 Jan 2026
Published online: 29 May 2026 *

Title: Integrating data mining techniques for analysing implicit user behaviours in online courses

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