Dynamic Systems View of Learning a Three-Tiered Theory in Physics: Robust Learning Outcomes as Attractors

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Abstract

The process of learning scientific knowledge from the dynamic systems viewpoint is studied in terms probabilistic learning model (PLM), where learning accrues from foraging in the epistemic landscape. The PLM leads to the formation of attractor-type regions of preferred models in an epistemic landscape. The attractor-type states correspond to robust learning outcomes which are more probable than others. These can be assigned either to the high confidence in model selection or to the dynamic evolution of a learner's proficiency, which depends on the learning history. The results suggest that robust learning states are essentially context dependent, and that learning is a continuous development between these context dependent states. (c) 2016 Wiley Periodicals, Inc. Complexity 21: 259-267, 2016
Original languageEnglish
JournalComplexity
Volume21
Issue numberS2
Pages (from-to)259–267
Number of pages9
ISSN1076-2787
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 114 Physical sciences

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