Expediting Support for Social Learning with Behavior Modeling

Jo Yohan, Gaurav Tomar, Oliver Ferschke, Carolyn Penstein Rose, Dragan Gasevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, our work proposes a three-part pipeline that includes data infrastructure, learning process analysis with behavior modeling, and intervention for support. We also describe an application of the pipeline to data from a social learning platform to investigate appropriate goal-setting
behavior as a qualification of role models. We find that students who follow appropriate goal setters persist longer in the course, show increased engagement in hands-on course activities, and are more likely to review previously covered materials as they continue through the course. To foster this
beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in finding qualified goal setters as role models. We discuss how this generalizable pipeline can be adapted for other support needs in online learning settings.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Educational Data Mining (EDM 2016)
PublisherInternational Educational Data Mining Society
Publication statusPublished - 2 Jul 2016

Keywords

  • educational data mining
  • learning analytics
  • self-regulated learning
  • social learning
  • ProSolo
  • text mining

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