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1

Department of Teaching and Learning, Washington State University, 332 Cleveland Hall, Pullman, WA 99164-1227, USA

2

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

Abstract 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





Autor: Richard Lamb 1,* and Joshua Premo 2

Fuente: http://mdpi.com/



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