The Significant Factors Affecting Students’ Academic Performance in Online Class: Multiple Linear Regression Approach
Keywords:
Academic Performance, COVID-19, Multiple Linear Regression (MLR), Online Distance Learning (ODL), StudentsAbstract
The COVID-19 pandemic, which began in Wuhan City, China in 2020, has thrown Malaysia's academic
sector into disarray. Students' academic performance changes dramatically when they move from faceto-face classes to full implementation of online distance learning (ODL). The purpose of this study is to
investigate the factors that affect students' academic performance during the COVID-19 pandemic
using Multiple Linear Regression (MLR). The research was carried out at UiTM Perlis Branch, and 54
bachelor's degree students from four faculties were invited to take part. During the analysis, gender,
hours students spent in online learning, hours students spent on preparation before class, number of
subjects taken, credit hours, hometown areas, and internet connection, act as independent variables
whereas CGPA as the dependent variable, were examined. This study was carried out using SPSS
software and Excel. The result shows that the hometown areas and hours students spent preparing
before class contributed significantly to the model while others did not. It is shown that students who
live in rural areas did much better in academic performance than students who live in cities, and the
more students spend on preparing themselves before class, the lower is their CGPA. Other factors tend
to be insignificant and it might be because of the limited time in collecting data, small sample size and
unequally-sized groups. For future research, it is recommended to have more time in collecting data
and add more sample sizes by extending it to diploma students to gain more accurate results.
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