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

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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.


Presented at the 9th International Conference on Augmented Cognition, Los Angeles, CA, August 2-7, 2015.



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