Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many op...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-08, Vol.2022, p.1-14
Main Authors: Ahmad, Sadique, Ben Aoun, Najib, Affendi, Mohammed A. El, Anwar, M. Shahid, Abbas, Sidra, Latif, Ahmed A. Abd El
Format: Article
Language:English
Subjects:
Accuracy
At risk students
Cognition & reasoning
Cognitive ability
Coronaviruses
COVID-19
Data analysis
Digitization
Frustration
Iterative methods
Machine learning
Mathematical models
Online instruction
Optimization
Performance evaluation
Performance prediction
Predictions
Probability
Psychology
Students
Synchronism
Synchronization
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Summary:Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These 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, including 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. This 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. The 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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/3183492