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International Conference on Artificial Intelligence in Education

AIED 2015: Artificial Intelligence in Education pp 772–775Cite as

Supporting Students’ Interactions over Case Studies

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9112))

Abstract

Having students analyze case studies is one of the common tutoring strategies for ill-defined domains. This paper presents a system, Umka, that supports learners as they form arguments about case studies in a professional ethics class. The system creates visualizations of the differences between students’ case positions, and suggests to students certain peers to interact with who might help to provide deeper and broader insight. The system has been run in several iterations. Experiments with the latest version confirm the benefits of the approach in stimulating productive interactions among students, and causing students to reconsider and broaden their initial positions on a case study.

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References

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Author information

Authors and Affiliations

  1. ARIES Lab, Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

    Mayya Sharipova & Gordon McCalla

Authors
  1. Mayya Sharipova
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  2. Gordon McCalla
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Corresponding author

Correspondence to Mayya Sharipova .

Editor information

Editors and Affiliations

  1. University of British Columbia, Vancouver, British Columbia, Canada

    Cristina Conati

  2. Computer Science Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Neil Heffernan

  3. Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand

    Antonija Mitrovic

  4. E.T.S.I. Informática, Universidad National de Educacion a Distancia, Madrid, Spain

    M. Felisa Verdejo

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© 2015 Springer International Publishing Switzerland

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Sharipova, M., McCalla, G. (2015). Supporting Students’ Interactions over Case Studies. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_113

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_113

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19772-2

  • Online ISBN: 978-3-319-19773-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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