doi: 10.18178/ijiet.2017.7.6.904
Participation Prediction and Opinion Formation in MOOC Discussion Forum
- 1School of Computer Science, Northeast Normal University, China
- 2School of Software Engineering, Northeast Normal University, China
Abstract
With the development of MOOCs, millions of students enrolled into online courses. The discussion forums in MOOCs provide a virtual community for students to interact with each other. The communication in different topics indicates the engagement of students in the courses and social-learning process during the interactions. In this respect, this paper explores the use of Naive Bayesian classification approach for predicting the participation of the forum and the use of Bayesian-based social-learning approach for modelling the opinion formation process during the discussion and indicating the influence of instructors in the discussion forum. Results on data from 1 Coursera course demonstrate that the poster’s retention can be well predicted by Naive Bayes classifier based on the combination of different features of the forum postings; additionally, we find that the superposters may not be the participants who will continue posting in the last several weeks. In terms of social-learning, our analysis indicates participants will aggregate information by repeated interactions and the instructors’ post can improve the convergence of learning process to the true belief. These results confirm the influence of the instructors’ intervention further.
Keywords
- Discussion forum
- MOOCs
- participation prediction
- opinion formation
How to Cite
Tieying Zhu, Wei Wang, Wei Zhao, and Riming Zhang, "Participation Prediction and Opinion Formation in MOOC Discussion Forum," International Journal of Information and Education Technology, vol. 7, no. 6, pp. 417-423, 2017. https://doi.org/10.18178/ijiet.2017.7.6.904
Copyright & License
Copyright © 2017 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).