The Application of Multiobjective Evolutionary Algorithms to an Educational Computational Model of Science Information Processing: a Computational Experiment in Science Education
作者： Richard L. Lamb , Jonah B. Firestone
DOI: 10.1007/S10763-015-9705-7
关键词: Research question 、 Learning sciences 、 Computational learning theory 、 Cognition 、 Information processing 、 Mathematics education 、 Science education 、 Evolutionary algorithm 、 Cognitive load 、 Psychology
摘要: Conflicting explanations and unrelated information in science classrooms increase cognitive load decrease efficiency learning. This reduced ultimately limits one’s ability to solve reasoning problems the science. In reasoning, it is of students sift through identify critical pieces that paramount importance Unfortunately, accomplish identification ideas not one develops without practice assistance form teachers or tutors classroom. The purpose this paper examine how application an evolutionary algorithm works within a computational model classroom simulate human for research purposes. question is: does combination optimization algorithms successfully mimic biological teaching learning systems classroom? Within study, author outlines simulates effects on “virtual” student task. Using STAC-M completes experiment examines role retraining also discusses important limitations powerful new tool.
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