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Presentation
Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment
Lecture Notes in Computer Science
  • Othalia Larue, Wright State University - Main Campus
  • Ion Juvina, Wright State University - Main Campus
  • Gary R. Douglas, Wright State University - Main Campus
  • Albert Simmons
Document Type
Conference Proceeding
Publication Date
1-1-2015
Abstract
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.
Comments

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

DOI
10.1007/978-3-319-20816-9_43
Citation Information
Othalia Larue, Ion Juvina, Gary R. Douglas and Albert Simmons. "Predicting Learner Performance Using a Paired Associate Task in a Team-Based Learning Environment" Lecture Notes in Computer Science Vol. 9183 (2015) p. 449 - 460 ISSN: 9783319208152
Available at: http://works.bepress.com/ion_juvina/46/