Expediting support for social learning with behavior modeling

Yohan Jo, Gaurav Tomar, Oliver Ferschke, Carolyn P Rosé, Dragan Gašević

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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, we propose 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. Students following appropriate goal setters persisted longer in the course, showed increased engagement in hands-on course activities, and were more likely to review previously covered materials as they continued through the course. To foster this beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in interacting with 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
EditorsTiffany Barnes, Min Chi, Mingyu Feng
Place of PublicationMassachusetts USA
PublisherInternational Educational Data Mining Society
Number of pages6
Publication statusPublished - 2016
Externally publishedYes
EventEducational Data Mining 2016 - Sheraton Raleigh Hotel, Raleigh, United States of America
Duration: 29 Jun 20162 Jul 2016
Conference number: 9th


ConferenceEducational Data Mining 2016
Abbreviated titleEDM 2016
Country/TerritoryUnited States of America
Internet address

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