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Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

by Richard Lamb 1,* and Joshua Premo 2
Department of Teaching and Learning, Washington State University, 332 Cleveland Hall, Pullman, WA 99164-1227, USA
School of Biological Sciences, Washington State University, 297 Eastlick Hall, Pullman, WA 99164-1227, USA
Author to whom correspondence should be addressed.
Academic Editor: Demos T. Tsahalis
Computation 2015, 3(3), 427-443;
Received: 7 May 2015 / Accepted: 28 August 2015 / Published: 2 September 2015
(This article belongs to the Section Computational Biology)
Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling. View Full-Text
Keywords: cognition; computational model; teaching and learning; science education cognition; computational model; teaching and learning; science education
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Lamb, R.; Premo, J. Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms. Computation 2015, 3, 427-443.

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