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Connecting the Dots Towards Collaborative AIED: Linking Group Makeup to Process to Learning

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Book cover Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10947))

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Abstract

We model collaborative problem solving outcomes using data from 37 triads who completed a challenging computer programming task. Participants individually rated their group’s performance, communication, cooperation, and agreeableness after the session, which were aggregated to produce group-level measures of subjective outcomes. We scored teams on objective task outcomes and measured individual students’ learning outcomes with a posttest. Groups with similar personalities performed better on the task and had higher ratings of communication, cooperation, and agreeableness. Importantly, greater deviation in teammates’ perception of group performance and higher ratings of communication, cooperation, and agreeableness negatively predicted individual learning. We discuss findings from the perspective of group work norms and consider applications to intelligent systems that support collaborative problem solving.

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Acknowledgements

We would like to thank David Blair, Eugene Choi, Mae Raab, and Samantha Scaglione for their invaluable help. This research was supported by the National Science Foundation (NSF DUE-1745442) and the Institute of Educational Sciences (IES R305A170432). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. University of Colorado Boulder, Boulder, CO, 80309, USA

    Angela Stewart & Sidney K. D’Mello

Authors
  1. Angela Stewart
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  2. Sidney K. D’Mello
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Correspondence to Angela Stewart .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen, Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Stewart, A., D’Mello, S.K. (2018). Connecting the Dots Towards Collaborative AIED: Linking Group Makeup to Process to Learning. 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_40

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  • DOI: https://doi.org/10.1007/978-3-319-93843-1_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93842-4

  • Online ISBN: 978-3-319-93843-1

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