Predictive Modeling of Student Engagement and Behavioral Outcomes Using Machine Learning Techniques in Technology-Enhanced Classrooms

The rapid integration of digital technologies into educational environments has generated vast amounts of learner interaction data, creating new opportunities for data-driven insights into student engagement and behavioral outcomes. This review paper examines the application of machine learning techniques for predictive modeling of student engagement, academic behaviors, and learning trajectories in technology-enhanced classrooms. It synthesizes existing literature on data sources such as learning management systems, intelligent tutoring systems, classroom sensors, and educational software platforms, highlighting how these multimodal data streams enable fine-grained behavioral analysis. The paper critically evaluates supervised, unsupervised, and deep learning approaches including regression models, decision trees, ensemble methods, neural networks, and temporal models for predicting engagement patterns, dropout risks, and behavioral shifts. Attention is given to model interpretability, fairness, and ethical considerations, particularly in high-stakes educational decision-making. Furthermore, the review discusses how predictive insights can inform adaptive interventions, personalized learning pathways, and early-warning systems that support student success. By consolidating methodological advances, challenges, and future research directions, this study provides a structured foundation for researchers, educators, and policymakers seeking to leverage machine learning to enhance learning experiences and behavioral outcomes in digitally mediated educational settings.