Enhanced random forest-based teaching practice and analysis for learning behaviour prediction

Educational data mining has emerged as a pivotal discipline for enhancing pedagogical practices through data-driven insights. While significant progress has been achieved in analysing learning behaviours, accurate prediction of teaching practices and student dynamics remains challenged by data complexity, behavioural variability, and methodological limitations of conventional approaches. This paper presents an enhanced random forest-based predictive method that systematically addresses these challenges through three key innovations. First, advanced feature selection mechanisms for handling high-dimensional educational data; second, optimised ensemble learning architecture improving prediction reliability; third, multi-source data integration capabilities enabling comprehensive behavioural analysis. Moreover, from the experimental results, the proposed method demonstrates over 15% higher accuracy and over 20% improvement compared to the state-of-the-art methods in terms of both the performance and cross-validation evaluations.