Evaluating Student Engagement and Retention Through Educational Data Mining

The effectiveness of Educational Data Mining (EDM) in the assessment of student engagement as well as the pre-forecasting of academic retention in high-level education is explored in this study. Using machine learning, deep learning and deep learning algorithms like the Random Forest, Neural Networks, K-means, and the BIRCH the study proves that the EDM could detect learners at risk early and assist in timely and personalized interventions. Multimodal data (behavioral, physiological and contextual data) and Learning Analytics (LA) will be incorporated into the conceptual framework together with sophisticated feature selection models such as DE-FS to ensure a more accurate prediction and generation of insight. The study further presents the prospect of the joint application of the qualitative methods; e.g., image segmentation and generative AI to determine cognitive interest based on content created by a student. Cross-cultural and contextual flexibility are also the priorities of the framework, and various issues are associated with such terms as generalizability, real-time use, and ethical data utilization. Findings validate the idea that EDM has a transformational opportunity to inject academic planning, curriculum design, and support systems of students. Finally, the study favors preventive, analytics-based forms of education that can enhance participation and high levels of retention and provide the equal access to a quality learning process in the various types of educational facilities.