  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.
