Scalable and Efficient Student Behavior Prediction using Parallelized Clustering and AHP-weighted KNN

This study proposes a scalable and efficient approach for predicting student behaviour in large-scale educational environments. It introduces a parallelized hybrid model that combines Density-Based Optimized K-Means clustering, Analytic Hierarchy Process (AHP) feature weighting, and Hierarchical K-Nearest Neighbours (KNN), implemented using Apache Spark. The main research question is how to improve scalability, accuracy, and computational efficiency of student behaviour prediction when dealing with large, complex datasets. The model addresses key limitations of traditional methods, such as handling heterogeneous data, treating all features equally, and high computational cost. Two main innovations are presented. First, AHP is used to assign structured importance to features, allowing critical factors like attendance and study time to have greater influence on prediction accuracy. Second, clustering and prediction are parallelized using Spark, enabling efficient real-time processing of large datasets. The approach was evaluated using 18,586 student records and more than 20 million behavioural entries. Results show that Hierarchical KNN consistently outperforms standard KNN as dataset size increases. While traditional KNN shows unstable error rates, peaking at 9.4%, Hierarchical KNN maintains lower and more stable errors between 5.16% and 6.08%. Execution time was also significantly reduced through parallel processing, though gains were limited by communication overhead. Overall, the proposed model offers a robust framework for real-time behaviour analysis, academic risk detection, and targeted educational intervention.