Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction

Abstract

In universities using the academic credit system, choosing elective courses is a crucial task that significantly affects student performance. Because of poor performances, numerous students have been receiving formal warnings and expulsions from universities. Certainly, a good study plan from course recommendation methods plays an important role in obtaining a good study performance. In addition, early warnings that release on challenging courses enable students to prepare better for such courses. Predicting student learning performance is a vital factor in the courses recommendation system and is an essential task of an academic advisor. Many research methods solved this problem with diverse approaches such as association rules, deep learning, and recommender systems (RS). It recently built the courses recommendation system, which is used for personalized recommendation, especially the matrix factorization (MF) technique; But, the prediction accuracy of the MF still need to be improved. So, many studies try to integrate more information (e.g., social networks, course relationships) into the model. Besides, deep learning addresses the student performance prediction, which currently is state of the art, but it usually is general rules (not a personalized prediction). Indeed, deep learning and matrix factorization have advantages and disadvantages, so they need to compound together to get better. This paper proposes an approach to predict student performance that utilizes the deep learning architecture to carry out the MF method to enhance prediction accuracy, called deep matrix factorization. Experimental results of the proposed approach are positive when we perform on the published educational dataset.