影响网络课堂学生学习成绩的显著因素:多元线性回归方法

Norwaziah Mahmud, Nur Syuhada Muhammat Pazil, Nur Afifah Nazurah Azman
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引用次数: 1

摘要

自2020年在中国武汉爆发新冠肺炎疫情以来,马来西亚学术界陷入了混乱。当学生从面对面的课程过渡到全面实施在线远程学习(ODL)时,他们的学习成绩发生了巨大的变化。本研究的目的是利用多元线性回归(MLR)研究新冠肺炎大流行期间影响学生学习成绩的因素。这项研究是在墨尔本科技大学珀斯分校进行的,来自四个学院的54名学士学位学生被邀请参加。在分析中,性别、学生在线学习时数、学生课前准备时数、选修科目数量、学分、家乡地区和网络连接作为自变量,而CGPA作为因变量。本研究采用SPSS软件和Excel软件进行。结果表明,学生的家乡地区和课前准备时间对模型有显著贡献,而其他因素对模型没有显著贡献。研究表明,生活在农村地区的学生在学习成绩上比生活在城市的学生要好得多,而且学生在课前准备上花费的越多,他们的CGPA就越低。其他因素往往不显著,这可能是因为收集数据的时间有限,样本量小,群体大小不等。在未来的研究中,建议增加收集数据的时间,增加样本量,将其扩展到毕业班学生,以获得更准确的结果。
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The Significant Factors Affecting Students’ Academic Performance in Online Class: Multiple Linear Regression Approach
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 face-to-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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