Examining Self-Regulated Learner Profiles in MOOCs: An Item Response Theory Perspective (Poster 20)

• In Event: AERA Poster Session Sixteen
  In Poster Session: Instructional Technology SIG Posters

Abstract

Self-regulated learning is critical for learners to succeed in MOOCs, but low self-regulated learners need support to regulate their learning process in MOOCs. Designing self-regulated learning interventions builds upon an accurate appraisal of learners’ self-regulated learning. Therefore, this study applied a two-parameter item response theory model to accurately evaluate online learners’ self-regulated learning in MOOCs and then performed a cluster analysis to identify learner profiles in terms of their traits in each phase of self-regulated learning. The findings of this study identified five clusters of self-regulated learner profiles, based upon which a further statistical analysis result indicated that the learners with lower forethought skills might be less committed to completing course assignments. Implications for self-regulated learning in MOOCs are discussed.

Authors

• Hengtao Tang, University of South Carolina
  Presenting Author

• Yu Bao, James Madison University
  Presenting Author