Data mining and network analysis in MOOCs

Data mining and network analysis in MOOCs

Massive Open Online Courses (MOOCs) provide a wealth of data, for example by recording interactions between learners. This is a quickly growing area of research, with the promise of better understanding learning experiences. Two approaches have been primarily employed so far: Social Network Analysis (SNA) and Machine Learning (ML). For example, they can respectively be used to assess learner-learner interactions (Tawfik et al., 2017), identify the type of forum posts that learners create, or examine the exact effects of innovations in pedagogy. This interdisciplinary project combines our expertise in computational techniques applied to human behaviors with the research on MOOCs conducted by Dr. Andrew Tawfik. Most recently, we (i) applied ML to understand how the level of learning progresses through a series of learner-learner interactions, and (ii) examined the differences between groups of learners taught using success or failure based cases.

Key reference:

  • Tawfik, A.A., Reeves, T.D., Stich, A.E., Gill, A., Hong, C., McDade, J., Pillutla, V.S., Zhou, X., Giabbanelli, P.J. (2017) The nature and level of learner–learner interaction in a chemistry massive open online course (MOOC). Journal of Computing in Higher Education.

Key collaborator:

  • Dr. Andrew Tawfik, Northern Illinois University, USA