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Modeling Metacognitive Activities in Medical Problem-Solving with BioWorld

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Metacognition: Fundaments, Applications, and Trends

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 76))

Abstract

Medical diagnostic reasoning is ill-defined and complex, requiring novice physicians to monitor and control their problem-solving efforts. Self-regulation is critical for effective medical problem-solving, helping individuals progress towards a correct diagnosis through a series of actions that informs subsequent ones. BioWorld is a computer-based learning environment designed to support novices in developing medical diagnostic reasoning as they receive feedback in the context of solving virtual cases. The system provides tools that scaffold learners in their requisite cognitive and metacognitive activities. Novices attain higher levels of competence as the system dynamically assesses their performance against expert solution paths. Dynamic assessment in this system relies on a novice-expert overlay and it is used to develop feedback when novices request help. When help-seeking occurs, help is provided by the tutoring module which applies a set of pre-defined rules based on the context of the learner’s activity. The system also provides cumulative feedback by comparing the novice solution with an expert solution following completion of the case. This chapter covers the essential design guidelines of this scaffolding approach to metacognitive activities in problem-solving within the domain of medical education. Specifically, we review recent advances in modeling metacognition through online measures, including concurrent think-aloud protocols, video-screen captures, and log-file entries. Educational data mining techniques are outlined with the goals of capturing metacognitive activities as they unfold throughout problem solving, and guiding the design of scaffolding tools in order to promote higher levels of competence in novices.

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Abbreviations

ANN:

Artificial Neural Networks

HMM:

Hidden Markov Models

MNB:

Multinomial Naïve Bayes

NB:

Naïve Bayes

SMO:

Sequential Minimal Optimization

TRE:

Technology-Rich Learning Environment

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Author information

Authors and Affiliations

  1. Department of Educational and Counselling Psychology, McGill University, 3700 McTavish Street, Montreal, QC, H3A 1Y2, Canada

    Susanne P. Lajoie, Tenzin Doleck & Amanda Jarrell

  2. Advanced Instructional Systems and Technologies Laboratory, University of Utah Educational Psychology, 1721 Campus Center Drive SAEC 3220, Salt Lake City, UT, 84112-8914, USA

    Eric G. Poitras

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

Correspondence to Susanne P. Lajoie .

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Editors and Affiliations

  1. World Outreach Light to the Nations Ministries, Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco, Mexico City, Mexico

    Alejandro Peña-Ayala

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Lajoie, S.P., Poitras, E.G., Doleck, T., Jarrell, A. (2015). Modeling Metacognitive Activities in Medical Problem-Solving with BioWorld. In: Peña-Ayala, A. (eds) Metacognition: Fundaments, Applications, and Trends. Intelligent Systems Reference Library, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-11062-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-11062-2_13

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