A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic

Saud Altaf, Rimsha Asad, Shafiq Ahmad, Iftikhar Ahmed, Mali Abdollahian and Mazen Zaindin
Additional contact information
Rimsha Asad: University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, Pakistan
Shafiq Ahmad: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Iftikhar Ahmed: Environmental and Public Health Department, College of Health Sciences, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates
Mali Abdollahian: School of Science, College of Sciences, Technology, Engineering, Mathematics, RMIT University, P.O. Box 2476, Melbourne, VIC 3001, Australia
Mazen Zaindin: Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

Sustainability, 2023, vol. 15, issue 15, 1-24

Abstract: COVID-19’s rapid spread has disrupted educational initiatives. Schools worldwide have been implementing more possibilities for distance learning because of the worldwide epidemic of the COVID-19 virus, and Pakistan is no exception. However, this has resulted in several problems for students, including reduced access to technology, apathy, and unstable internet connections. It has become more challenging due to the rapid change to evaluate students’ academic development in a remote setting. A hybrid deep learning approach has been presented to evaluate the effectiveness of online education in Pakistan’s fight against the COVID-19 epidemic. Through the use of multiple data sources, including the demographics of students, online activity, learning patterns, and assessment results, this study seeks to realize the goal of precision education. The proposed research makes use of a dataset of Pakistani learners that was compiled during the COVID-19 pandemic. To properly assess the complex and heterogeneous data associated with online learning, the proposed framework employs several deep learning techniques, including 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. With the 98.8% accuracy rate for the trained model, it was clear that the deep learning framework could beat the performance of any other models currently in use. It has improved student performance assessment, which can inform tailored learning interventions and improve Pakistan’s online education. Finally, we compare the findings of this study to those of other, more established studies on evaluating student progress toward educational precision.

Keywords: deep learning framework; educational data mining; online learning; learning analytics; COVID-19; classification framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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