Meaningful Learner Information for MOOC Instructors Examined Through a Contextualized Evaluation Framework


  • Kerrie A Douglas Purdue University
  • Mitchell W. Zielinski Purdue University
  • Hillary Merzdorf Purdue University
  • Heidi A Diefes-Dux Purdue University
  • Peter Bermel Purdue University



MOOCs, Evaluation, MOOC Instructor


Improving STEM MOOC evaluation requires an understanding of the current state of STEM MOOC evaluation, as perceived by all stakeholders.  To this end, we investigated what kinds of information STEM MOOC instructors currently use to evaluate their courses and what kinds of information they feel would be valuable for that purpose.  We conducted semi-structured interviews with 14 faculty members from a variety of fields and research institutions who had taught STEM MOOCs on edX, Coursera, or Udacity.  Four major themes emerged related to instructors' desires: (1) to informally assess learners as an instructor might in a traditional classroom, (2) to assess learners’ attainment of personal learning goals, (3) to obtain in-depth qualitative feedback from learners, and (4) to access more detailed learner analytics regarding the use of course materials.  These four themes contribute to a broader sentiment expressed by the instructors that they have access to a wide variety of quantitative data for use in evaluation, but are largely missing the qualitative information that plays a significant role in traditional evaluation.  Finally, we provide our recommendations for MOOC evaluation criteria, based on these findings.

Author Biographies

Kerrie A Douglas, Purdue University

Prof. Kerrie Douglas is an Assistant Professor of Engineering Education at Purdue University. Her research is focused on methods of evaluation and assessment in large educational environments.

Hillary Merzdorf, Purdue University

Engineering Education



How to Cite

Douglas, K. A., Zielinski, M. W., Merzdorf, H., Diefes-Dux, H. A., & Bermel, P. (2019). Meaningful Learner Information for MOOC Instructors Examined Through a Contextualized Evaluation Framework. The International Review of Research in Open and Distributed Learning, 20(1).



Research Articles