Enhanced System for Prediction of Students’ Performance Using Deep Learning

This work created an improved deep learning model for predicting student academic achievement. Students' data is gathered from online learning platforms, offline learning systems in schools, or through questions and responses. Many writers could use the collected data to better understand student behavior and, as a result, improve learning levels and student performance. To serve as legitimate input for a deep learning model, the gathered data must be processed first. The deep learning model's hyper-parameters were optimized using a genetic method. The OULAD dataset was utilized for validation. The results demonstrated that CNN plus a genetic algorithm is an effective strategy for predicting academic success.