Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Mac
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Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning Richard Lamb1 · Brian Hand1 · Amanda Kavner1 Accepted: 25 September 2020 © Springer Nature B.V. 2020
Abstract This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test. The Student Task and Cognition Model in this study uses cognitive data from a large-scale randomized control study. Results of the computational model experiment provide for the possibility to increase student success via targeted cognitive retraining of specific cognitive attributes via the SWH. This study also illustrates that computational modeling using machine learning algorithms (MLA) is a significant resource for testing educational interventions, informs specific hypotheses, and assists in the design and development of future research designs in science education research. Keywords Machine learning · Computational modeling · Cognition · Critical thinking
Introduction Developing working memory and subordinate processes such critical thinking in conjunction with the scientific practices associated with reasoning is a critical objective for educators in science. Critical thinking is a higher order cognitive function and domain of working memory which has broad applicability across the practices and content of multiple disciplines within science. The working memory system is seen as a core cognitive function for successful learning and information processing in science learning through writing (Ab Kadir 2018). A key mechanism to promote the development of working memory and subsidiary functioning of learning through writing in science uses interventions such as the Science Writing Heuristic. Initial examinations of learning outcomes in the practices of argumentative writing * Richard Lamb [email protected] Brian Hand brian‑[email protected] Amanda Kavner [email protected] 1
College of Education, Neurocognition Science Laboratory, East Carolina University, 128 Rivers Building, Greenville, NC 27858, USA
illustrate that access to environments using written arguments to learn improves critical thinking among a number of other cognitive attributes (Lam et al. 2018). Notwithstanding the clear positives of writing to learn to develop cognitive functions associated with writing to learn in science, little is understood and known about how underlying cognitive mechanisms (structurally and functionally) associated with writing to learn promotes learning and critical thinking in science contexts. While cognitive and neuroscience researchers have attempted to close this gap, there has been little adoption of these models and techniques within educational research. The lack of adoption seems to result from the focus on theories of cognitive mechani
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