Evaluation and semantic approach for student performance prediction using data mining techniques

Abstract

Educational Data Mining (EDM) refers to the field that focuses on using various statistical methods and data mining techniques to draw inferences from large amount of educational data set. Over the years it has been used for student performance prediction by applying various machine learning processes and more recently deep learning. As a result of the lockdown announced after the spread of Covid-19, many colleges were forced to adapt to online learning tools. In this paper, we identify and evaluate the impact of the Covid-19 pandemic and its subsequent fallout in predicting student's academic performance. For this, a data set of various undergraduate students was compiled from March 2021. A Likert-type questionnaire was administered and large number of responses were gathered from various primary and secondary resources. This was subsequently used to validate the proposed methodology. Furthermore, different classification algorithms were used to predict the performance of the student and subsequently compared with one another based on their accuracy. The results show that the excessive use of e- learning tools including smartphones, laptops and tablets have a significant impact on student's academic performance as well as on their psychological health. The work will help us to better understand the impact of the lockdown on student's scholastic performance and point out areas where online-learning methods can be improved.