基于回归模型的新型冠状病毒肺炎期间高校学习成绩预测分析

M. Zulfikri, S. M. Shaharudin, N. A. Rajak, M. S. Ibrahim
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引用次数: 4

摘要

——2019年12月爆发的2019冠状病毒病(COVID-19)影响了包括教育机构学生在内的世界各地人们的生活方式。这些学生必须通过适应在线教学和学习方法,为学习的连续性做好心理和身体上的准备,这对他们的学习成绩会产生重大影响。因此,本研究考察了学生在网络教学方式下的学习成绩,从而预测他们在即将到来的学期的学习成绩。这项研究可以采取各种措施来提高和保持学生在学习活动中的表现。这项研究是在Pendidikan Sultan Idris大学科学与数学学院的本科生中进行的。本文提出了用线性回归模型预测学生学业成绩的方法。结果表明,在适应网络教学方式的过程中,学生的学习成绩不断提高。它还表明,在适应这种新规范方法的过程中,很少有受访者受到影响。因此,通过拥有更全面的马来西亚大学数据,可以改善回归模型的未来发展。©2021北大西洋大学联盟。版权所有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model
- 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. © 2021 North Atlantic University Union NAUN. All rights reserved.
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