Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students

Shi, Lei; Cristea, Alexandra I.; Toda, Armando; Oliveira, Wilk; Siarheyeva, A.; Barry, C.; Lang, M.; Linger, H.; Schneide, C.

Abstract

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex “big data” from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience.

Citation

Shi, L., Cristea, A. I., Toda, A., Oliveira, W., Siarheyeva, A., Barry, C., Lang, M., Linger, H., & Schneide, C. (2019, August). Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students. Presented at 28th International Conference on Information Systems Development (ISD2019), Toulon, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 28th International Conference on Information Systems Development (ISD2019)
• Start Date: Aug 28, 2019
• End Date: Aug 30, 2019
• Acceptance Date: Jun 1, 2019
• Online Publication Date: Nov 1, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Jul 3, 2019
• Publicly Available Date: Dec 10, 2019
• Publisher: Association for Information Systems
• Book Title: Information Systems Development : Information Systems Beyond 2020 (ISD2019 Proceedings).
• Public URL: https://durham-repository.worktribe.com/output/1142476
• Publisher URL: https://aisel.aisnet.org/isd2014/proceedings2019/ISDMethodologies/13/