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Towards Investigating Performance Differences in Clinical Reasoning in a Technology Rich Learning Environment

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Artificial Intelligence in Education (AIED 2015)

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

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Abstract

Technology Rich Learning Environments (TREs) are increasingly used to support scholastic activities. BioWorld is an example of a TRE designed to support the metacognitive activities of learners tasked with solving virtual patient cases. The present paper aims to examine the performance differences of novice physicians in diagnosing cases in BioWorld. We present an empirically guided line of research concerning the performance differences: (1) across three endocrinology cases, (2) between genders, (3) between goal orientations, and (4) in diagnosis correctness.

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Author information

Authors and Affiliations

  1. Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada

    Tenzin Doleck, Amanda Jarrell & Susanne Lajoie

  2. Department of Educational Psychology, University of Utah, Salt Lake City, UT, USA

    Eric Poitras

Authors
  1. Tenzin Doleck
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  2. Amanda Jarrell
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  3. Eric Poitras
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  4. Susanne Lajoie
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Corresponding author

Correspondence to Amanda Jarrell .

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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Doleck, T., Jarrell, A., Poitras, E., Lajoie, S. (2015). Towards Investigating Performance Differences in Clinical Reasoning in a Technology Rich Learning Environment. 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_63

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_63

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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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