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Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning

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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.

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Authors and Affiliations

  1. College of Education, Neurocognition Science Laboratory, East Carolina University, 128 Rivers Building, Greenville, NC, 27858, USA

    Richard Lamb, Brian Hand & Amanda Kavner

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  1. Richard Lamb
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Correspondence to Richard Lamb.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration, its later amendments, or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

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Lamb, R., Hand, B. & Kavner, A. Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning. J Sci Educ Technol 30, 283–297 (2021). https://doi.org/10.1007/s10956-020-09871-3

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