Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames
Richard L. Lamb, Washington State University, United States ; Leonard Annetta, George Mason University, United States ; David B. Vallett, University of Nevada, Las Vegas, United States ; Troy D. Sadler, University of Missouri, United States
Computers & Education Volume 70, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd
There has been an increase in student achievement testing focusing on content and not underlying student cognition. This is of concern as student cognition provided for a more generalizable analysis of learning. Through a cognitive diagnostic approach, the authors model the propagation of cognitive attributes related to science learning using Serious Educational Games. One-way to increase the focus on the cognitive aspects of learning that are additional to content learning is through the use cognitive attribute task-based assessments (Cognitive Diagnostics) using an Artificial Neural Network. Results of this study provide a means to examine underlying cognition which, influences successful task completion within science themed SEGs. Results of this study also suggest it is possible to define, measure, and produce a hierarchical model of latent cognitive attributes using a Q-matrix relating virtual SEGs tasks, which are similar to real-life tasks aiding in the modeling of transference.
Lamb, R.L., Annetta, L., Vallett, D.B. & Sadler, T.D. (2014). Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames. Computers & Education, 70(1), 92-104. Elsevier Ltd.
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Liz Owens Boltz, Brian Arnold & Spencer Greenhalgh, Michigan State University, United States
Society for Information Technology & Teacher Education International Conference 2015 (Mar 02, 2015) pp. 822–829
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