AI-Based Predictive Framework for Student Behavior Analysis and Performance Forecasting in Smart Learning Environments

Abstract

The advances in Artificial Intelligence (AI) and Machine Learning (ML) have transformed education, particularly in adaptive e-learning environments. Traditional teaching methodologies face challenges in monitoring student progress, adapting instruction, and optimizing the learning experience. The objective of this study is to design and validate an AI-driven Effective Teaching-Learning Model (AID-ETLM) that improves personalized learning through advanced Deep Neural Networks (DNNs). The materials and methods consist of a teacher-student DNN ensemble combined with a Knowledge-Distillation pipeline, evaluated on multi-modal interaction logs collected from a real e-learning platform, where engagement, learning preferences, and teacher-student interactions are extracted in real time. The model implements a dual-prediction mechanism that fuses forecast scores with example gradients to dynamically refine instructional strategies and drive adaptive content recommendation. The paper reports five performance dimensions: student performance ratio, efficiency ratio, evaluation-score analysis, student interaction ratio, and learning-style prediction. The results show that AID-ETLM improves student engagement by 27%, content-retention rate by 19%, and outperforms baseline frameworks including Smart Education Model (FSEM), Artificial Neural Network (ANN), Big-Data with AI Education (BD-AIE), and the Deep Learning Model (DLM). An ablation study, paired statistical-significance testing (p < 0.001), and computational-complexity analysis confirm that every component of the model contributes a measurable gain and that AID-ETLM remains practical for deployment on standard institutional servers. This suggests integrating pedagogical strategy with AI-driven adaptation to build a resilient, intelligent e-learning ecosystem that delivers consistent instructional quality, interactive student success, and a more engaging digital education paradigm.