Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment

  • Othalia LarueEmail author
  • Ion Juvina
  • Gary Douglas
  • Albert Simmons
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)


In this paper, we use a computational cognitive model to make a priori predictions for an upcoming human study. Model predictions are generated in conditions identical to those that human participants will be placed in. Models were built in a computational cognitive architecture, which implements a theory of human cognition, ACT-R (Adaptive Control of Thought - Rational) (Anderson, 2007). The experiment contains three conditions: lecture, interactive lecture, and team-based learning (TBL). Team-based learning has been shown to improve performance compared to the classical non-interactive lecture. Our model predicted the same outcome. It also predicted that players in the TBL condition would perform better than players in the interactive lecture condition.


Cognitive modeling Team-based learning A priori model prediction 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Othalia Larue
    • 1
    Email author
  • Ion Juvina
    • 1
  • Gary Douglas
    • 1
  • Albert Simmons
    • 1
  1. 1.Adaptive Strategic Thinking and Executive Control of Cognition and Affect (ASTECCA) LaboratoryWright State UniversityDaytonUSA

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