Student performance classification and prediction in fully online environment using Decision tree

Abstract

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary field of study that focuses on methodologies for extracting useful knowledge from data. During the latest Covid-19 pandemic, there was a significant uptick in online-based learning (e-learning) operations as every educational institution moved its operations to digital channels. To increase the quality of education in this new normal, it is necessary to determine the key factors in students' performance. The main objective of this study is to exploit the regulating factors of education via digital platforms during the covid-19 pandemic by extracting knowledge and a set of rules by using the Decision Tree (j48) classifier. In this study, we developed a conceptual framework using four datasets, each with a different set of attributes and instances, collected from "X-University"and Microsoft teams. 'Final term' and 'Mid-term' examinations acted as the root node for all four datasets. The findings of this study would benefit higher education institutions by helping instructors and students to recognize the shortcomings and influences controlling students' performance in the online platforms during the covid-19 pandemic, as well as serve as an early warning framework for predicting students' deficiencies and low school performance.