Research Article Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity Sadique Ahmad , 1,2 Najib Ben Aoun , 3,4 Mohammed A. El Affendi , 1 M. Shahid Anwar , 5 Sidra Abbas , 6 and Ahmed A. Abd El Latif 1,7 1 EIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia 2 Department of Computer Sciences, Bahria University, Islamabad, Karachi Campus, Pakistan 3 Department of Information Technology, College of Computer Science and Information Technology, Al-Baha University, Saudi Arabia 4 REGIM-Lab Research Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, BP 1173, Sfax 3038, Tunisia 5 Department of Artificial Intelligence and Software, Gachon University, Seongnam-Si, Republic of Korea 6 Department of Computer Science, COMSATS University, Sahiwal, Pakistan 7 Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Al Minufiyah, Shebeen El-Kom 32511, Egypt Correspondence should be addressed to Sadique Ahmad; saahmad@psu.edu.sa and M. Shahid Anwar; shahidanwar786@gmail.com Received 25 April 2022; Revised 4 June 2022; Accepted 8 July 2022; Published 16 August 2022 Academic Editor: Yousaf Bin Zikria Copyright © 2022 Sadique Ahmad et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). ese students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students’ performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students’ emotions, in- cluding frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students’ frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students’ frustration severity. is paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students’ performance via two modules. First, frustration is divided into four outer layers. Second, the students’ performance outcome is split into 34 inner layers. e prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures. 1. Introduction e outbreak of COVID-19 and E-learning with Massive Open Online Courses (MOOCs) introduced new challenges for weak students. ey brought significant changes in students’ lifestyles, academic teaching methodology, and performance evaluation procedures. COVID-19 has sig- nificantly increased students’ frustration as they struggle to achieve excellent grades and good employment opportu- nities [1–3]. A student with high frustration severity is likely to perform poorly in academic activities, e.g., assignments, quizzes, workshops, and examinations [4]. Also, they made optimization of students’ performance prediction systems more challenging. Such systems highlight at-risk students for psychological counseling and extra attention. Also, the prediction system needs optimized students’ frustration Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 3183492, 14 pages https://doi.org/10.1155/2022/3183492