MOOCEOLOGY

Project Name:
MOOCEOLOGY

Principal Investigator: Dr. Alyssa Wise

Researchers: Yi Cui, Jovita Vytasek & Wan Qi Jin

Project Overview

Massive Open Online Courses (MOOCs) are online learning environments with open enrollments that allow learners to use learning materials and participate in basic learning interaction activities for free. These courses usually attract a tremendous number of learners to start, with only a small percentage following through to completion. Learning interactions in MOOCs are distinct due to the extremely high learner-instructor ratio, diversity in learners’ background and intent, and tools available. The MOOCeology project studies the interaction practices in MOOCs and other large-scale learning environments based on analysis of the artifacts left behind by learners’ and instructors’ activity.

Project Goals

The MOOCeology project studies interactions in MOOCs by examining discussion forum activities to explore the characteristics of these activities and their impact on learning.

  1. Develop methods and tools to characterize different kinds of MOOC discussion interactions
  2. Investigate how interactions around the course material are initiated and develop in MOOC discussions
  3. Investigate how interactions around the course materials impact learning outcomes

Publications:

  • Cui, Y., Wise, A. F. & Jin, W.Q. (in review). Humans and machines together: Improving characterization of large scale online discussions through dynamic interrelated post and thread classification (DIPTiC).
  • Wise, A. F., Cui, Y. & Jin, W.Q. (in review). Honing in on social learning networks in MOOC forums: Examining critical network definition decisions.
  • Wise, A. F., Cui, Y., Jin, W.Q. & Vytasek, J. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. The Internet and Higher Education, 32, 11-28.
  • Wise, A. F., Cui, Y. & Vytasek, J. (2016). Bringing order to chaos in MOOC discussion forums with content-related thread identification. In Proceedings of the 6th International Conference on Learning Analytics and Knowledge (pp.188-197). ACM: NY. [Recorded Presentation]
  • Cui, Y. & Wise, A. F. (2015). Identifying content-related threads in MOOC discussion forums. In Proceedings of the 2nd ACM Conference on Learning @ Scale (299-303). ACM: NY.
  • Wise, A. F. (2014). Data archeology: A theory informed approach to analyzing data traces of social interaction in large scale learning environments. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) Workshop on Modeling Large Scale Social Interaction In Massively Open Online Courses, (pp. 1–2). Doha, Qatar: ACL.