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.
Douglas, G. R.,
& Simmons, A.
(2015). Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment. Lecture Notes in Computer Science, 9183, 449-460.
Presented at the 9th International Conference on Augmented Cognition, Los Angeles, CA, August 2-7, 2015.