Journal article
Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning
Journal of science education and technology, Vol.30(2), pp.283-297
04/01/2021
DOI: 10.1007/s10956-020-09871-3
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.
Details
- Title: Subtitle
- Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning
- Creators
- Richard Lamb - East Carolina UniversityBrian Hand - East Carolina UniversityAmanda Kavner - East Carolina University
- Resource Type
- Journal article
- Publication Details
- Journal of science education and technology, Vol.30(2), pp.283-297
- Publisher
- Springer Nature
- DOI
- 10.1007/s10956-020-09871-3
- ISSN
- 1059-0145
- eISSN
- 1573-1839
- Number of pages
- 15
- Language
- English
- Date published
- 04/01/2021
- Academic Unit
- Teaching and Learning
- Record Identifier
- 9984371090102771
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