Massive Open Online Courses (MOOCs) scale up their class size by eliminating synchronous teaching and the need for students and instructors to be co-located. Yet, the very characteristics that enable scalability of MOOCs also bring significant challenge to its teach. In particular, high student-to-instructor ratio in MOOCs restricts instructors’ ability to facilitate student learning by intervening in discussions forums, as they do in face-to-face classrooms — the lack of interaction and feelings of isolation have been attributed as reasons for why enrolled students drop from MOOCs.
Using a large sample of forum data culled from Coursera MOOCs we design predictive models to identify discussion threads for the instructor to intervene. Such machine learning models may allow building of dashboards to automatically prompt instructors on when and how to intervene in discussion forums. In the longer term, it may be possible to automate these interventions relieving instructors of this effort. Such automated approaches are essential for allowing good pedagogical practices to scale in the context of MOOC discussion forums.
MOOC Wikification is a spinoff from this project with similar objectives.
Proceedings of the Thirty-First AAAI conference on Artificial Intelligence (AAAI-17), 2017.
Proceedings of the 8th International Conference on Education Data Mining (EDM) 2015, 2015.
Proceedings of the International Conference on Information Systems (2015), 2015.
- Muthu Kumar Chandrasekaran (Project Lead)
- Min-Yen Kan (Professor and Advisor)
- Radhika Nikam (Research Assistant)
- An Yahui (Postgrad Intern)
- Divish Dayal (Undergrad Intern)
- Elavarasi Manogaran (Research Assistant – Alumna)
- You can process your Coursera’s SQL data dumps using our free, open source library lib4MOOCdata. This also serves as a codebase to replicate our published findings.
- Coursera Crawler: An Yahui, our visiting PhD intern has built this neat crawler: https://github.com/anyahui120/Coursera-Crawler to scrape Coursera discussion forums.
- 2 Jun 2017