Analysis of MOOCs Courses Dropout Rate Based on Students' Studying Behaviors

DOI

10.2991/hss-17.2017.26How to use a DOI?

Keywords

Analysis of Variance, Logistic Regression, Social Learning Networks, Dropout rate.

Abstract

This paper investigates course-dropout rate based on users' various behaviors-video-watching, discussion and question-answering behavior in MOOCs. We mainly analyze two problems: one is the comparison of average duration of studying on MOOC among users who either drop out or complete the course, and the other is to identify the exact relationship between these three behaviors and course completion rate, which in other words to predict the completion rate by means of students' studying behaviors. By applying analysis of variance and logistics regression, we draw several conclusions:(1) In high dropout rate courses, the difference between the behavior of all users regardless of whether they have dropped out or completed the course is not statistically significant, however, in contrast, among low dropout rate courses, there exists magnificent difference between the behaviors among those two kinds of users. (2) It is common both in high and low dropout rate courses that all users tend to spend less time studying as time goes on. (3)Considering all these behaviors, only video-watching behavior significantly and positively determines the completion rate: Considering the findings above, we suggest that it is useful to make improvement on videos to keep students motivated in the course, thereby boosting the completion rate.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - Fang-Jie Liu
AU  - Lu Wang
AU  - Yi-Jun Qian
AU  - Yu-Jie Wu
PY  - 2017/02
DA  - 2017/02
TI  - Analysis of MOOCs Courses Dropout Rate Based on Students' Studying Behaviors
BT  - Proceedings of the 2017 2nd International Conference on Humanities and Social Science (HSS 2017)
PB  - Atlantis Press
SP  - 139
EP  - 144
SN  - 2352-5398
UR  - https://doi.org/10.2991/hss-17.2017.26
DO  - 10.2991/hss-17.2017.26
ID  - Liu2017/02
ER  -