Novel Use of an Artificial Neural Network to Computationally Model Cognitive Processes in Science Learning
Journal Title: Computer Reviews Journal - Year 2019, Vol 3, Issue 0
Abstract
The purpose of this paper is to outline the creation of a computational model making use of an underlying processing element in the form of an artificial neural network (ANN). Within the study, the ANN models multiple conservation tasks as inputs from video game play during a high school science content learning game. This model is based upon the identification of cognitive attributes and integration of two advanced psychological and educational measurement theories. Using the approached of cognitive diagnostics, and item response theory (IRT) data examined for computational suitability. Once initial task response patterns are identified via IRT; the patterns are parametrized and presented to an artificial neural network (ANN) as probabilities. Using the ANN derived Student Task and Cognition Model (STAC-M); the study authors simulated a cognitive training intervention using 100,000 students in science classrooms. Results of the simulation suggest that it is possible to increase levels of student success using a targeted cognitive attribute approach and that computational modeling provides a means to develop future science education research and is a means to test current educational theory.
Authors and Affiliations
Richard Richard Lamb
Keywords
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