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Identifying middle school students’ challenges in computational thinking-based science learning
Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students’ learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302855/ https://www.ncbi.nlm.nih.gov/pubmed/30613246 http://dx.doi.org/10.1186/s41039-016-0036-2 |
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author | Basu, Satabdi Biswas, Gautam Sengupta, Pratim Dickes, Amanda Kinnebrew, John S. Clark, Douglas |
author_facet | Basu, Satabdi Biswas, Gautam Sengupta, Pratim Dickes, Amanda Kinnebrew, John S. Clark, Douglas |
author_sort | Basu, Satabdi |
collection | PubMed |
description | Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students’ learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to support synergistic learning remains an important challenge. Relatively, little is known about how a student’s conceptual understanding develops in such learning environments and the difficulties they face when learning with such integrated curricula. In this paper, we present a research study with CTSiM (Computational Thinking in Simulation and Modeling)—computational thinking-based learning environment for K-12 science, where students build and simulate computational models to study and gain an understanding of science processes. We investigate a set of core challenges (both computational and science domain related) that middle school students face when working with CTSiM, how these challenges evolve across different modeling activities, and the kinds of support provided by human observers that help students overcome these challenges. We identify four broad categories and 14 subcategories of challenges and show that the human-provided scaffolds help reduce the number of challenges students face over time. Finally, we discuss our plans to modify the CTSiM interfaces and embed scaffolding tools into CTSiM to help students overcome their various programming, modeling, and science-related challenges and thus gain a deeper understanding of the science concepts. |
format | Online Article Text |
id | pubmed-6302855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-63028552019-01-04 Identifying middle school students’ challenges in computational thinking-based science learning Basu, Satabdi Biswas, Gautam Sengupta, Pratim Dickes, Amanda Kinnebrew, John S. Clark, Douglas Res Pract Technol Enhanc Learn Research Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students’ learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to support synergistic learning remains an important challenge. Relatively, little is known about how a student’s conceptual understanding develops in such learning environments and the difficulties they face when learning with such integrated curricula. In this paper, we present a research study with CTSiM (Computational Thinking in Simulation and Modeling)—computational thinking-based learning environment for K-12 science, where students build and simulate computational models to study and gain an understanding of science processes. We investigate a set of core challenges (both computational and science domain related) that middle school students face when working with CTSiM, how these challenges evolve across different modeling activities, and the kinds of support provided by human observers that help students overcome these challenges. We identify four broad categories and 14 subcategories of challenges and show that the human-provided scaffolds help reduce the number of challenges students face over time. Finally, we discuss our plans to modify the CTSiM interfaces and embed scaffolding tools into CTSiM to help students overcome their various programming, modeling, and science-related challenges and thus gain a deeper understanding of the science concepts. Springer Singapore 2016-05-21 2016 /pmc/articles/PMC6302855/ /pubmed/30613246 http://dx.doi.org/10.1186/s41039-016-0036-2 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Basu, Satabdi Biswas, Gautam Sengupta, Pratim Dickes, Amanda Kinnebrew, John S. Clark, Douglas Identifying middle school students’ challenges in computational thinking-based science learning |
title | Identifying middle school students’ challenges in computational thinking-based science learning |
title_full | Identifying middle school students’ challenges in computational thinking-based science learning |
title_fullStr | Identifying middle school students’ challenges in computational thinking-based science learning |
title_full_unstemmed | Identifying middle school students’ challenges in computational thinking-based science learning |
title_short | Identifying middle school students’ challenges in computational thinking-based science learning |
title_sort | identifying middle school students’ challenges in computational thinking-based science learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302855/ https://www.ncbi.nlm.nih.gov/pubmed/30613246 http://dx.doi.org/10.1186/s41039-016-0036-2 |
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