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

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.