Ari Krakowski, Eric Greenwald, Natalie Roman, Christina Morales, Suzanna Loper
{"title":"Computational Thinking for Science: Positioning coding as a tool for doing science","authors":"Ari Krakowski, Eric Greenwald, Natalie Roman, Christina Morales, Suzanna Loper","doi":"10.1002/tea.21907","DOIUrl":null,"url":null,"abstract":"<p>The role of computation in science is ever-expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real-world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10-lesson instructional units for middle-school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.</p>","PeriodicalId":48369,"journal":{"name":"Journal of Research in Science Teaching","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.21907","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research in Science Teaching","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/tea.21907","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
The role of computation in science is ever-expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real-world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10-lesson instructional units for middle-school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.
期刊介绍:
Journal of Research in Science Teaching, the official journal of NARST: A Worldwide Organization for Improving Science Teaching and Learning Through Research, publishes reports for science education researchers and practitioners on issues of science teaching and learning and science education policy. Scholarly manuscripts within the domain of the Journal of Research in Science Teaching include, but are not limited to, investigations employing qualitative, ethnographic, historical, survey, philosophical, case study research, quantitative, experimental, quasi-experimental, data mining, and data analytics approaches; position papers; policy perspectives; critical reviews of the literature; and comments and criticism.