Computational Intelligence Enabled Student Performance Estimation in the Age of COVID-19.

Abstract

The sudden advent of COVID-19 pandemic left educational institutions in a difficult situation for the semester evaluation of students; especially where the online participation was difficult for the students. Such a situation may also happen during a similar disaster in the future. Through this work, we want to study the question: can the deep learning methods be leveraged to predict student grades based on the available performance of students. To this end, this paper presents an in-depth analysis of deep learning and machine learning approaches for the formulation of an automated students’ performance estimation system that works on partially available students’ academic records. Our main contributions are: (a) a large dataset with 15 courses (shared publicly for academic research); (b) statistical analysis and ablations on the estimation problem for this dataset; (c) predictive analysis through deep learning approaches and comparison with other arts and machine learning algorithms. Unlike previous approaches that rely on feature engineering or logical function deduction, our approach is fully data-driven and thus highly generic with better performance across different prediction tasks. The main takeaways from this study are: (a) for better prediction rates, it is desirable to have multiple low weightage tests than few very high weightage exams; (b) the latent space models are better estimators than sequential models; (c) deep learning models have the potential to very accurately estimate the student performance and their accuracy only improves as the training data are increased.