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IJIET 2017 Vol.7(6): 417-423 ISSN: 2010-3689
doi: 10.18178/ijiet.2017.7.6.904

Participation Prediction and Opinion Formation in MOOC Discussion Forum

Tieying Zhu, Wei Wang, Wei Zhao, and Riming Zhang

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.

Index Terms—Discussion forum, MOOCs, participation prediction, opinion formation.

Tieying Zhu, Wei Wang, and Wei Zhao are with School of Computer Science, Northeast Normal University, China (e-mail: zhuty@nenu.edu.cn, wangw@nenu.edu.cn, zhaow@nenu.edu.cn).
Riming Zhang is with School of Software Engineering, Northeast Normal University, China (e-mail: zhangrm281@nenu.edu.cn).


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.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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