BDEN: Big Data-Driven Intelligent Tutoring Systems for Personalized Learning Analytics

Abstract

Objectives: To develop BDEN, a scalable intelligent tutoring framework for personalized learning analytics, learner mastery prediction, and early identification of at-risk students. Method: BDEN integrates Kafka–Spark distributed processing with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) model for knowledge tracing. The framework was evaluated using 4.2 million learning interactions from 18,600 learners across 1,240 knowledge concepts and compared with six baseline models. Findings: BDEN achieved 91.4% accuracy, 91.0% precision, 90.2% recall, 90.6% F1-score, AUC-ROC of 0.947, and RMSE of 0.189. Performance improvements were statistically significant (p < 0.001), and scalability testing achieved approximately 74,600 interaction records per second. Novelty: BDEN uniquely combines distributed big-data processing, multi-source educational feature integration, attention-enhanced BiLSTM knowledge tracing, learner-state feedback, and prerequisite-aware recommendations in a unified scalable framework for personalized and adaptive learning. Keywords: Big Data Analytics, Intelligent Tutoring Systems, Knowledge Tracing, Deep Learning, Educational Data Mining