Student Performance Prediction Using Atom Search Optimization Based Deep Belief Neural Network

Abstract

Abstract Student performance prediction is a primary goal for educational institutions to improve future performance of students. Early prediction of student’s future performance is critical and it may result interventions to students. Researchers focused in this field and attempt to give a better prediction at early stage. Previously existing algorithms namely fuzzy logic was implemented. However, these algorithms failed to explore this prediction problem. In this paper, a deep learning perspective is proposed for this prediction problem. Deep learning-based algorithm termed as Deep Belief Neural Network (DBNN) with Atom Search Optimization (ASO) optimization categorised students based on their historical performance. DBNN works through a cognitive divergence algorithm with multiple cascaded Restricted Boltzmann Machines (RBM). Initially the proposed model utilized an open-source educational dataset. It is pre-processed in first phase and classified in the second phase. In second phase, DBNN finds quite challenging to optimize the learning rate parameters. By optimization using ASO, the prediction of student level is done automatically. To evaluate the proposed work, performance metrics are measured and provided better result than previous algorithms. Performance of this proposed model outperforms to accuracy level of 90% and reduced error value below 20%. It is concluded that the proposed method of student performance prediction in the academic field is helpful to both students and academicians.