Computational Thinking for Science Positions Youth to Be Better Science Learners
Computational thinking plays a central and ubiquitous role in many science disciplines and is increasingly prevalent in science instruction and learning experiences. This study empirically examines the computational thinking skills that are particular to engaging in science and science learning and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/1/105 |
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| author | Matthew A. Cannady Melissa A. Collins Timothy Hurt Ryan Montgomery Eric Greenwald Rena Dorph |
| author_facet | Matthew A. Cannady Melissa A. Collins Timothy Hurt Ryan Montgomery Eric Greenwald Rena Dorph |
| author_sort | Matthew A. Cannady |
| collection | DOAJ |
| description | Computational thinking plays a central and ubiquitous role in many science disciplines and is increasingly prevalent in science instruction and learning experiences. This study empirically examines the computational thinking skills that are particular to engaging in science and science learning and then tests if these skills are predictive of science learning over the course of one semester. Using a sample from 600 middle school science students, we provide the psychometric properties of a computational thinking for science assessment and demonstrate that this construct is a consistent predictor of science content learning. The results demonstrate that the relationship between computational thinking for science and science content learning is consistent across variations in students and classrooms, above and beyond other demonstrated predictors—STEM fascination or scientific sensemaking. Further, the analysis also showed that experience with computer programming languages, especially block languages, is associated with higher levels of computational thinking. The findings reveal implications for research, teaching, and learning, including some implications for advancing equitable opportunities for students to develop computational thinking for science. This paper advances knowledge about how to ensure that students have the dispositions, skills, and knowledge needed to use technology-enabled scientific inquiry practices and to position them for success in science learning. |
| format | Article |
| id | doaj-art-72fc7c6cb68d40fb8adebd79a3af0172 |
| institution | Kabale University |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-72fc7c6cb68d40fb8adebd79a3af01722025-01-24T13:30:37ZengMDPI AGEducation Sciences2227-71022025-01-0115110510.3390/educsci15010105Computational Thinking for Science Positions Youth to Be Better Science LearnersMatthew A. Cannady0Melissa A. Collins1Timothy Hurt2Ryan Montgomery3Eric Greenwald4Rena Dorph5Lawrence Hall of Science, University of California, Berkeley, CA 94720, USALawrence Hall of Science, University of California, Berkeley, CA 94720, USALawrence Hall of Science, University of California, Berkeley, CA 94720, USALawrence Hall of Science, University of California, Berkeley, CA 94720, USALawrence Hall of Science, University of California, Berkeley, CA 94720, USALawrence Hall of Science, University of California, Berkeley, CA 94720, USAComputational thinking plays a central and ubiquitous role in many science disciplines and is increasingly prevalent in science instruction and learning experiences. This study empirically examines the computational thinking skills that are particular to engaging in science and science learning and then tests if these skills are predictive of science learning over the course of one semester. Using a sample from 600 middle school science students, we provide the psychometric properties of a computational thinking for science assessment and demonstrate that this construct is a consistent predictor of science content learning. The results demonstrate that the relationship between computational thinking for science and science content learning is consistent across variations in students and classrooms, above and beyond other demonstrated predictors—STEM fascination or scientific sensemaking. Further, the analysis also showed that experience with computer programming languages, especially block languages, is associated with higher levels of computational thinking. The findings reveal implications for research, teaching, and learning, including some implications for advancing equitable opportunities for students to develop computational thinking for science. This paper advances knowledge about how to ensure that students have the dispositions, skills, and knowledge needed to use technology-enabled scientific inquiry practices and to position them for success in science learning.https://www.mdpi.com/2227-7102/15/1/105computational thinkingactivationSTEM learningK-12 learninginstrument designscience learning |
| spellingShingle | Matthew A. Cannady Melissa A. Collins Timothy Hurt Ryan Montgomery Eric Greenwald Rena Dorph Computational Thinking for Science Positions Youth to Be Better Science Learners Education Sciences computational thinking activation STEM learning K-12 learning instrument design science learning |
| title | Computational Thinking for Science Positions Youth to Be Better Science Learners |
| title_full | Computational Thinking for Science Positions Youth to Be Better Science Learners |
| title_fullStr | Computational Thinking for Science Positions Youth to Be Better Science Learners |
| title_full_unstemmed | Computational Thinking for Science Positions Youth to Be Better Science Learners |
| title_short | Computational Thinking for Science Positions Youth to Be Better Science Learners |
| title_sort | computational thinking for science positions youth to be better science learners |
| topic | computational thinking activation STEM learning K-12 learning instrument design science learning |
| url | https://www.mdpi.com/2227-7102/15/1/105 |
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