The effectiveness of using deep learning algorithms in predicting students achievements - Mohammed Akour; Hiba Al Sghaier; Osama Al Qasem (Indonesian Journal of Electrical Engineering and Computer Science, 2020)
Educational Data Mining (EDM)  research has taking an important place as it helps in exposing useful knowledge from educational data sets to be employed and serve several purposes such as predicting students’ achievements. Predicting student’s achievements might be useful for building and adopting several changes in the educational environments as a re-action in the current educational systems. Most of the existing research have used machine learning to predict students’ achievements by using diverse attributes such as family income, students gender, students absence and level etc.  In this paper, the effort is made to explore the effectiveness of using the deep learning algorithm more precisely CNN to predict students’ achievements which could hlp in predicting if student will be able to finish their degree or not.  The experimental results reveal how the proposed model outperformed the existing approaches in terms of prediction accuracy.