(untitled) (Beacon: International Journal of Education and Social Studies, 2025)
The rapid adoption of Learning Management Systems (LMSs) in higher education has generated large volumes of educational data that can be utilized to improve teaching and learning processes. However, the increasing complexity of learner behavior and the diversity of educational interactions present significant challenges for accurately predicting student performance and learning outcomes. This study aims to develop a deep learning-based framework for constructing accurate predictive models within LMS environments. The proposed framework integrates learner interaction logs, assessment results, engagement metrics, and behavioral indicators to identify learning patterns and forecast academic performance. A literature-based approach was employed to examine recent developments in LMS analytics, adaptive learning, educational data mining, and deep learning applications in education. The findings indicate that deep learning techniques, particularly Deep Neural Networks (DNNs), outperform conventional machine learning approaches in handling high-dimensional educational datasets and predicting learner outcomes. Furthermore, the integration of predictive analytics, adaptive learning, recommender systems, and sentiment analysis can significantly enhance personalized learning experiences and support timely educational interventions. The proposed framework contributes to the development of intelligent LMS environments capable of improving learning effectiveness, student retention, and data-driven decision-making in higher education.