Title: Academic students' performance prediction model: an Oman case study

Authors: P. Vijaya; Satish Chander; S.L. Gupta

Addresses: Modern College of Business and Science, P.O. Box No-100, Al-Khuwair, PC-133, Sultanate of Oman ' Waljat College of Applied Sciences, P.O. Box 197, P.C. 124, Rusayl, Muscat, Sultanate of Oman ' Waljat College of Applied Sciences, P.O. Box 197, P.C. 124, Rusayl, Muscat, Sultanate of Oman

Abstract: The education system in Oman attains a fast growth, and it requires effective standards to increase the number of graduates with quality education and with effective skills and knowledge. The higher education institutions (HEIs) in Oman are increasing in number, and it poses the need for graduates with world-level competing tendencies. Keeping these in mind, the proposed methodology proposes a novel method of predicting the academic performance of the students' enrolled in the universities of Oman. For predicting the academic performance of the students, the dragonfly optimisation-based deep belief network (DrDBN) is employed. The data is collected using the proper questionnaire session, and the best feature is selected based on the fuzzy-based entropy function. The training algorithm determines the optimal weight to the deep belief network (DBN) for predicting the best solution. The proposed method predicts the performance of the student in the semester exams and adopts a proper teaching standard to equally benefit the students of all grades, and also, the prediction strategy contributes a lot to the students to utilise their full potentials in the process of learning. The effectiveness of the proposed DrDBN is checked depending on the MSE and the RMSE metric values and is evaluated to be the best when compared to other existing techniques with low MSE value as 0.532, and low RMSE value as 0.026, respectively.

Keywords: student performance prediction; dragonfly algorithm; fuzzy entropy; deep belief network; DBN; MSE.

DOI: 10.1504/IJIDS.2021.116499

International Journal of Information and Decision Sciences, 2021 Vol.13 No.2, pp.166 - 191

Received: 28 Jan 2019
Accepted: 12 Jun 2019
Published online: 27 Jul 2021 *

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