Open AccessPredictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model
Author(s) -
Muhammad Zulfikri,
Shazlyn Milleana Shaharudin,
Noorazrin Abd Rajak,
Muhammad Ibrahim
Publication year - 2021
Publication title -
international journal of biology and biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.149
H-Index - 6
ISSN - 1998-4510
DOI - 10.46300/91011.2021.15.21
Subject(s) - covid-19 , learning analytics , regression analysis , higher education , mathematics education , psychology , medical education , norm (philosophy) , computer science , medicine , infectious disease (medical specialty) , data science , political science , machine learning , disease , pathology , law
The coronavirus disease 2019 (COVID-19) outbreak in December 2019 had affected the way of living for people around the world including students in educational institutions. These students had to prepare for the continuity of their study mentally and physically by adapting to the online teaching and learning approach, which can significantly impact their academic performance. Hence, this study examines the students' academic performance on the online teaching and learning approach, thus predicting their academic performance for the upcoming semester. This study enables various actions to be taken in improving and maintaining the student's performance in their study activities. The study was conducted on undergraduate students from the Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris. This paper presents the prediction of the student's academic performance with a linear regression model. Evidently, the result shows that the student's academic performance continually improves while adapting to the online teaching and learning approach. It also shows that there were few respondents affected while adapting to this new norm approach. Hence, the future development of the regression model can be improved by having a more comprehensive range of Malaysian universities' data.