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https://doi.org/10.1007/978-3-319-39690-3_33

has been cited by the following article:

• TITLE: Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering

  AUTHORS: Guanggong Ge, Quanlong Guan, Lusheng Wu, Weiqi Luo, Xingyu Zhu

  KEYWORDS: Online Learning, K-Prototypes Clustering, Economically Developed Region, Data Analysis, Different Groups, Learning Behavior, Learning Media

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.10 No.1, January 30, 2022

  ABSTRACT: Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use questionnaires designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning result than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.