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

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. © 2023 by the authors.

Keywords Classification framework, COVID-19, Deep learning framework, Educational data mining, Learning analytics