Unveiling Student Success: A Multifaceted Approach with Learning Coefficients and Beyond

The student performance is examined in this study using a number of methods of Educational Data Mining (EDM), Clustering and classification techniques are employed to classify the course as well as the performance in the entrance examination. The results obtained show that the Random Forest and XG Boost which are machine learning models outperform traditional methods for predicting student success. Moreover, CNN and LSTM Networks, which are deep learning models, improve prediction accuracy even further. Conducted through metrics like accuracy, precision, recall and Fl-score, this study shows that any form of recognition of the pattern, in this case, the early one, helps to reduce failure rates to considerable extents. The results of this study suggest that there is a potential scope for further improving prediction algorithms and management of educational resources, which are of great relevance to the institutions to further the student success.