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.