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.