Predicting Student Performance With an Extended Crisscross and Guided Learning Enhanced Machine Learning Model

Abstract

Accurately predicting postgraduate student performance is crucial for effective pedagogical interventions to enhance future learning performance. The significant variability in university subjects and institutions complicates this prediction process. This paper proposes a reliable machine learning model, bEGWOA-FKNN, to address these challenges by predicting student performance based on previous academic records, thereby facilitating satisfactory graduation. The EGWOA algorithm, which introduced the extended crisscross strategy and guided learning strategy to the whale optimisation algorithm (WOA), is employed to select feature subsets from a dataset collected at Wenzhou University. Its global search capability is validated through extensive experiments, including quality analysis, ablation studies and comparisons with state-of-the-art algorithms. The bEGWOA-FKNN model, which integrates the binary version of the EGWOA (bEGWOA) algorithm with the fuzzy k-nearest neighbour, selects the most informative features and achieves superior accuracy compared to several existing feature selection methods.