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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Rahman, Md. Ataur
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Computer Science & IT Engineering

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Machine learning based COVID-19 study performance prediction Rahman, Md. Ataur; Rahman, Md. Sadekur; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1130-1139

Abstract

COVID-19 has impacted education worldwide. In this troublesome situation, it is hard enough for an institution to predict a student’s performance. Students’ performance prediction has always been a complex task and this pandemic situation has led this task to be more complex. The main focus of this work is to come up with a machine learning model based on a classical machine learning technique to predict the change in students’ performance due to COVID-19. Initially, some relevant features are selected, based on which the data are collected from students of some private universities in Bangladesh. After the entire data set is formed, we preprocessed the dataset to remove redundancy and noise. These preprocessed data are used for testing and training using the proposed model. The model is extensively evaluated in this way using three separate classical machine learning techniques, namely linear regression, k-nearest neighbors (k-NN), and decision tree. Finally, the results of the entire experiment follow, demonstrating the power of the machine learning model in such an application. It is observed that the proposed model with linear regression exhibits the best performance with an R2 error of 0.07% and an accuracy of 99.84%.
Co-Authors Habib, Md. Tarek Hasan, Mahady Islam, Mohammad Monirul Md. Sadekur Rahman, Md. Sadekur