Computational Models for Academic Performance Estimation
It addresses a domain-specific issue for students and faculty during remote learning, but is incremental as it builds on existing data-driven approaches.
The paper tackles the problem of estimating students' academic performance during the COVID-19 pandemic by developing an automated system using deep learning and machine learning on partially available records, achieving better performance across tasks compared to previous methods.
Evaluation of students' performance for the completion of courses has been a major problem for both students and faculties during the work-from-home period in this COVID pandemic situation. 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 fifteen 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.