Hybrid Model to Predict the Performance of Students

This study utilizes machine learning and deep learning algorithms, including the Decision Tree, Support Vector Machine (SVM), Conventional Neural Network (CNN), Long Short-Term Memory (LSTM), and a novel Hybrid Machine Learning model to predict student performance. The performance of these models is assessed and compared based on their accuracy. Notably, the Hybrid Machine Learning model achieved the highest accuracy, demonstrating the potential of hybrid modeling in educational data mining. While the study primarily aims to enhance the precision of student performance prediction, it also critically examines the pros and cons of various machine learning and deep learning techniques in the context of educational settings. Moreover, the study underscores the importance of further research in this area, focusing on model interoperability and generalization across different datasets. It is expected that the insights gained from this study will guide educators and researchers in refining student performance predictions, thereby bolstering student success