Open AccessStudent performance classification and prediction in fully online environment using Decision tree
Author(s) -
Musaddiq Al Karim,
Mst. Yeasmin Ara,
Md. Mahadi Masnad,
Mostafa Rasel,
Dip Nandi
Publication year - 2021
Publication title -
the aiub journal of science and engineering
Language(s) - English
Resource type - Journals
eISSN - 2520-4890
pISSN - 1608-3679
DOI - 10.53799/ajse.v20i3.173
Subject(s) - c4.5 algorithm , computer science , exploit , decision tree , decision tree learning , machine learning , knowledge extraction , educational data mining , data science , classifier (uml) , multidisciplinary approach , data mining , artificial intelligence , naive bayes classifier , support vector machine , computer security , social science , sociology
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.