Modeling Metacognitive Activities in Medical Problem-Solving with BioWorld

  • Susanne P. Lajoie
  • Eric G. Poitras
  • Tenzin Doleck
  • Amanda Jarrell
Chapter

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.

Keywords

Tools Scaffolding approaches ITS Metacognition Problem-solving Bioworld Medical education Novice-expert overlay Help-seeking 

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Susanne P. Lajoie
    • 1
  • Eric G. Poitras
    • 2
  • Tenzin Doleck
    • 1
  • Amanda Jarrell
    • 1
  1. 1.Department of Educational and Counselling PsychologyMcGill UniversityMontrealCanada
  2. 2.Advanced Instructional Systems and Technologies LaboratoryUniversity of Utah Educational PsychologySalt Lake CityUSA

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