An Effective Method for Creating a Learning Management Systems (LMS) Prediction Model using Deep Learning Methods.

This study explores the improvement of a learning management system (LMS) prediction model utilizing deep learning methods to improve educational outcomes in numerous learning environments. Transitioning from traditional to digital learning has posed challenges, inclusive of decreased flexibility and student engagement. Our research delves into those transitions' impacts on student success, focusing on customized learning studies, effective resource provisioning, and predictive analysis the usage of advanced computational methods. We employed deep learning techniques to analyze data from different learning modalities, together with cloud-based platforms and blended learning contexts, to expect pupil performance and resource desires appropriately. The evaluation involved neural networks and machine learning algorithms like Random forest, implemented to datasets from instructional settings to identify at-risk students and optimize learning management structures for better instructional consequences. [ABSTRACT FROM AUTHOR]