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When Optimal Team Formation Is a Choice - Self-selection Versus Intelligent Team Formation Strategies in a Large Online Project-Based Course

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Artificial Intelligence in Education (AIED 2018)

Abstract

Prior research in Team-Based Massive Open Online Project courses (TB-MOOPs) has demonstrated both the importance of effective group composition and the potential for using automated methods for forming effective teams. Past work on automated team assignment has produced both spectacular failures and spectacular successes. In either case, different contexts pose particular challenges that may interfere with the applicability of approaches that have succeeded in other contexts. This paper reports on a case study investigating the applicability of an automated team assignment approach that has succeeded spectacularly in TB-MOOP contexts to a large online project-based course. The analysis offers both evidence of partial success of the paradigm as well as insights into areas for growth.

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Notes

  1. 1.

    http://oli.cmu.edu/.

  2. 2.

    http://www.cs.cmu.edu/~msakr/15619-f17/.

  3. 3.

    https://theproject.zone/.

  4. 4.

    https://piazza.com/.

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Acknowledgement

This work was funded in part by the NIH grant 5R01HL122639-03 and NSF grants IIS-1546393 and ACI-1443068. The research reported here was further enriched by being conducted in the context of the Program in Interdisciplinary Education Research (PIER), a training grant to Carnegie Mellon University funded by the Institute of Education Sciences (R305B150008). The opinions expressed are those of the authors and do not represent the views of the Institute, the U.S. Department of Education or other funding organizations that supported this work.

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Correspondence to Sreecharan Sankaranarayanan .

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Sankaranarayanan, S., Dashti, C., Bogart, C., Wang, X., Sakr, M., Rosé, C.P. (2018). When Optimal Team Formation Is a Choice - Self-selection Versus Intelligent Team Formation Strategies in a Large Online Project-Based Course. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-93843-1_38

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