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ERIC Number: ED592733
Record Type: Non-Journal
Publication Date: 2016
Pages: 6
Abstractor: As Provided
Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses
Wen, Miaomiao; Maki, Keith; Wang, Xu; Dow, Steven P.; Herbsleb, James; Rose, Carolyn
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016)
To create a satisfying social learning experience, an emerging challenge in educational data mining is to automatically assign students into effective learning teams. In this paper, we utilize discourse data mining as the foundation for an online team-formation procedure. The procedure features a deliberation process prior to team assignment, where participants hold discussions both to prepare for the collaboration task and provide indicators that are then used during automated team assignment. We automatically assign teams in a way that maximizes average observed pairwise transactivity exchange within teams, whereas in a control condition, teams are assigned randomly. We validate our team-formation procedure in a crowdsourced online environment that enables effective isolation of variables, namely Amazon's Mechanical Turk. We compare group knowledge integration outcomes between the two team assignment conditions. Our results demonstrate that transactivity-based team assignment is associated with significantly greater knowledge integration (p < 0.05, effect size 3 standard deviations). [For the full proceedings, see ED592609.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A