Social Interactions Clustering MOOC Students: An Exploratory Study

Shi, Lei; Cristea, Alexandra I.; Toda, Armando M.; Oliveira, Wilk; Ahmad, Alamri; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus

Abstract

An exploratory study on social interactions of MOOC students in FutureLearn was conducted, to answer "how can we cluster students based on their social interactions?" Comments were categorized based on how students interacted with them, e.g., how a student's comment received replies from peers. Statistical modelling and machine learning were used to analyze comment categorization, resulting in 3 strong and stable clusters.

Citation

Shi, L., Cristea, A. I., Toda, A. M., Oliveira, W., Ahmad, A., Chang, M., Sampson, D. G., Huang, R., Hooshyar, D., Chen, N.-S., Kinshuk, & Pedaste, M. (2020, December). Social Interactions Clustering MOOC Students: An Exploratory Study. Presented at The 20th International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 20th International Conference on Advanced Learning Technologies (ICALT)
• Acceptance Date: Mar 9, 2020
• Online Publication Date: Aug 4, 2020
• Publication Date: 2020
• Deposit Date: Aug 11, 2020
• Publicly Available Date: Dec 4, 2020
• Pages: 172-174
• Series ISSN: 2161-3761,2161-377X
• Book Title: IEEE 20th International Conference on Advanced Learning Technologies ICALT 2020 ; proceedings.
• ISBN: 9781728160917
• Keywords: Learning analytics, Clustering, Social interaction
• Public URL: https://durham-repository.worktribe.com/output/1142399
• Publisher URL: https://ieeexplore.ieee.org/xpl/conhome/9146898/proceeding?pageNumber=1