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Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames

, Washington State University, United States ; , George Mason University, United States ; , University of Nevada, Las Vegas, United States ; , 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. Retrieved November 26, 2020 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

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