Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories

DOI

10.2991/assehr.k.200723.102How to use a DOI?

Keywords

MOOC, Completer, Learning analysis, Kmeans, Behavior pattern

Abstract

The learning energy of MOOC completers has important reference value for future learners. Existing research focuses on the behavioral performance of dropouts and participants, but ignores the mining of completer behavior patterns. In this paper, Kmeans clustering method, descriptive statistics, one-way analysis of variance, and chi-square test were used to systematically study the behavioral characteristics and academic performance of 1,388 MOOC completers. The results show that there are significant differences in resource and task preferences, effort levels, and so on, and learners can be divided into hard-working harvesters and punch-in participants; there is no significant difference in demographic characteristics. The article proposes strategies for improving teaching and promoting ordinary learners to improve learner behavior patterns and learning efficiency.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Changsheng Chen
AU  - Jingyun Long
AU  - Junxiao Liu
AU  - Zongjun Wang
AU  - Minglei Shan
AU  - Yuming Dou
PY  - 2020
DA  - 2020/07/24
TI  - Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories
BT  - Proceedings of the 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020)
PB  - Atlantis Press
SP  - 56
EP  - 63
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200723.102
DO  - 10.2991/assehr.k.200723.102
ID  - Chen2020
ER  -