Mapping of Students’ Academic Performance in Online Learning Environment during Pandemic Using Multiple Correspondence Analysis

Ainil Fauzani Rosmadi, S. M. Shaharudin, Murugan Rajoo, R. Tarmizi, M. S. Samsudin
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引用次数: 1

Abstract

The virtual and digital learning process seems to hugely impact academic achievement due to the COVID-19 outbreak, globally. Thus, improving student performance is one of the important focuses of educational management. Mapping students’ actual conditions is a mandatory requirement before designing the performance improvement program. Therefore, this study proposed a statistical investigation to map out students’ performance and the problems they encountered during online learning using Multiple Correspondence Analysis (MCA), revealing the hidden pattern and classifying students based on their demographics (programs, CGPA, and origin) and learning environments. The data samples consist of 234 undergraduate students in the Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). The study findings formed from two different profiles where each profile has its respective categories. The study found that the students of the Bachelor of Education (Mathematics) with Honour, categorized as smart students, preferred to study face-to-face because of poor internet connection from using mobile data. On the other hand, the students of the Bachelor of Science (Mathematics) with Education, who were categorized as average students, had no difficulty continuing either synchronous or asynchronous online learning in the future because of stable internet access using their home Wi-Fi connection. Moreover, the preference made was also due to family interruption issues.
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基于多重对应分析的流感大流行期间在线学习环境下学生学习成绩映射
由于新冠肺炎疫情的爆发,虚拟和数字学习过程似乎对全球的学习成绩产生了巨大影响。因此,提高学生成绩是教育管理的重要重点之一。在设计绩效改进方案之前,了解学生的实际情况是一项强制性要求。因此,本研究提出了一项统计调查,利用多重对应分析(Multiple Correspondence Analysis, MCA)来绘制学生在网络学习中的表现和遇到的问题,揭示隐藏的模式,并根据学生的人口统计学(专业、CGPA、来源)和学习环境对学生进行分类。数据样本包含了Pendidikan Sultan Idris大学(UPSI)科学与数学学院数学系的234名本科生。研究结果来自两个不同的概况,其中每个概况都有其各自的类别。研究发现,被归类为聪明学生的荣誉教育学士(数学)的学生更喜欢面对面学习,因为使用移动数据的互联网连接不佳。另一方面,被归类为普通学生的教育学理学学士(数学)学生在未来继续进行同步或异步在线学习没有任何困难,因为他们使用家庭Wi-Fi连接稳定的互联网接入。此外,做出的选择也是由于家庭干扰问题。
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CiteScore
2.80
自引率
0.00%
发文量
120
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