Enhancing Personalised Learning with Graph-Based Ensemble Prediction and Skill Cluster Mapping for Student Knowledge Completeness

The increasing adoption of data-driven educational systems requires reliable methods to predict student readiness for future coursework and support personalised learning pathways. This study proposes a graph-enhanced ensemble framework that integrates curriculum structure and skill-gap awareness to estimate student course readiness. A global prerequisite directed acyclic graph (DAG) of university subjects was constructed to model curriculum dependencies, from which structural features including the PageRank, in-degree, out-degree, and prerequisite chain depth were derived. In parallel, a domain-informed skill cluster mapping grouped subjects into nine interpretable competency domains to enable skill-gap analysis. These curriculum-aware features were combined with academic history, behavioural engagement, and demographic indicators to produce 38 engineered features for each student–subject pair. Six models (CatBoost, XGBoost, LightGBM, FT-Transformer, MLP and TabPFN) were trained and combined using a weighted ensemble. Experiments on a real-world institutional dataset containing 20,581 students and 727,168 records achieved an AUC of 0.8908 for predicting course success. Ablation experiments demonstrate that graph-derived and skill-cluster features provide modest but statistically significant incremental value. The resulting model was integrated into a prototype personalised recommender that prioritizes curriculum-consistent learning pathways. The proposed framework provides an interpretable and curriculum-aware approach for personalised learning. While the model demonstrates strong overall performance, a moderate gender disparity in the false positive rate was observed. Results were obtained on a large longitudinal dataset from a single university, and external validation at other institutions is needed to confirm generalizability.