Computational Thinking Ability of Grade IV Elementary School Students on Energi and Its Changes Material
DOI:
https://doi.org/10.56916/jirpe.v4i4.2542Keywords:
Computational Thinking, Elementary School, energy transformation, science educationAbstract
Despite the emphasis on higher-order thinking skills in 21st-century learning, computational thinking skills among elementary school students remain underdeveloped. Many students rely on memorization instead of understanding the logical, systematic process of solving problems, particularly in science. This is evident in their inability to break down complex problems into smaller parts, identify interrelationships between components, and develop structured, step-by-step solution strategies. This indicates a gap between future competency needs and classroom learning practices. This study aims to examine and describe the computational thinking abilities of fourth-grade students in relation to energy and its changes. The study will determine if there is a problem with students' computational thinking abilities in this context. This study employed a descriptive quantitative method with a sample of 80 students from several elementary schools. The instrument used was a written test based on computational thinking indicators, including decomposition, pattern recognition, abstraction, and algorithms. The results showed that students' computational thinking ability was poor because the value range was 62. Students can identify patterns and organize problem-solving steps systematically. However, they still have difficulty abstracting information and breaking down complex problems into simpler parts. These findings underscore the importance of integrating learning activities that stimulate computational thinking skills early on, particularly in elementary school science education, and of applying appropriate models to enhance students' computational thinking abilities.
References
Aksit, O., & Wiebe, E. N. (2020). Exploring force and motion concepts in middle grades using computational modeling: A classroom intervention study. Journal of Science Education and Technology, 29(1), 65–82. https://doi.org/10.1007/s10956-019-09800-z
Angelia, Y., Supeno, & Suparti. (2022). Science process skills of elementary school students in science learning using inquiry learning model. Basicedu Journal, 6(5), 8109–8118. https://doi.org/10.31004/basicedu.v6i5.3692
Barron, B., & Darling-Hammond, L. (2008). Teaching for meaningful learning: A review of research on inquiry-based and cooperative learning. In L. Darling-Hammond, B. Barron, P. D. Pearson, A. H. Schoenfeld, E. K. Stage, T. D. Zimmerman, G. N. Cervetti, & J. L. Tilson (Eds.), Powerful learning: What we know about teaching for understanding (pp. 11–70). Jossey-Bass. https://eric.ed.gov/?id=ED539399
Basogain Olabe, X., Olabe Basogain, M. Á., Olabe Basogain, J. C., Rico Alonso, M. J., Amórtegui Rodríguez, M., & Ramírez Uclés, R. (2017). Pensamiento computacional en las escuelas de Colombia: Colaboración internacional de innovación en la educación [Computational thinking in schools of Colombia: International collaboration for innovation in education]. RED: Revista de Educación a Distancia, 17(55), 1–20. https://recursos.educoas.org/sites/default/files/5188.pdf
Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and practice in technology enhanced learning, 11(1), 13. https://doi.org/10.1186/s41039-016-0036-2
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education: Implications for policy and practice. European Commission, Joint Research Centre. https://doi.org/10.2791/792158
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association (pp. 1–25). https://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
Cabrera, L., Ketelhut, D. J., Mills, K., Killen, H., Coenraad, M., Byrne, V. L., & Plane, J. D. (2024). Designing a framework for teachers' integration of computational thinking into elementary science. Journal of Research in Science Teaching, 61(6), 1326–1361. https://doi.org/10.1002/tea.21888
Chen, C. H., & Yang, Y. C. (2019). Revisiting the effects of project-based learning on students' academic achievement: A meta-analysis investigating moderators. Educational Research Review, 26, 71–81. https://doi.org/10.1016/j.edurev.2018.11.001
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students' computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
Chen, R. F., Eisenkraft, A., Fortus, D., Krajcik, J. S., Neumann, K., Nordine, J., & Scheff, A. (Eds.). (2014). Teaching and learning of energy in K-12 education. Springer. https://doi.org/10.1007/978-3-319-05017-1
Cole, L. B., Fallahhosseini, S., Zangori, L., & Oertli, R. T. (2023). Learnscapes for renewable energy education: An exploration of elementary student understanding of solar energy systems. Interdisciplinary Journal of Environmental and Science Education, 19(2), e2305. https://doi.org/10.29333/ijese/13034
Condliffe, B., Quint, J., Visher, M. G., Bangser, M. R., Drohojowska, S., Saco, L., & Nelson, E. (2017). Project-based learning: A literature review. MDRC.
Cui, Z., & Ng, O. L. (2021). The interplay between mathematical and computational thinking in primary school students' mathematical problem-solving within a programming environment. Journal of Educational Computing Research, 59(5), 988–1012. https://doi.org/10.1177/0735633120979930
Demir, Ö., & Seferoğlu, S. S. (2019). Developing a Scratch-based coding achievement test. Information and Learning Sciences, 120(5/6), 383-406. https://doi.org/10.1108/ILS-08-2018-0078
Ezeamuzie, N. O., & Leung, J. S. C. (2022). Computational thinking through an empirical lens: A systematic review of literature. Journal of Educational Computing Research, 60(2), 427–460. https://doi.org/10.1177/07356331211033158
Griffin, P., & Care, E. (Eds.). (2015). Assessment and teaching of 21st century skills: Methods and approach. Springer. https://doi.org/10.1007/978-94-017-9395-7
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Handayani, L., Ardiansyah, D., & Susanti, E. (2020). Computational thinking in primary school learning: A literature review. Basicedu Journal, 4(4), 963–970. https://doi.org/10.31004/basicedu.v4i4.465
Hestness, E., Ketelhut, D. J., McGinnis, J. R., & Plane, J. (2018). Professional knowledge building within an elementary teacher professional development experience on computational thinking in science education. Journal of Technology and Teacher Education, 26(3), 411–435. https://www.learntechlib.org/primary/p/181431/
ISTE. (2016). ISTE standards for students. International Society for Technology in Education. https://www.iste.org/standards/iste-standards-for-students
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263–279. https://doi.org/10.1016/j.compedu.2014.11.022
Juwantara, R. A. (2019). Analisis teori perkembangan kognitif Piaget pada tahap anak usia operasional konkret 7-12 tahun dalam pembelajaran matematika. Al-Adzka: Jurnal Ilmiah Pendidikan Guru Madrasah Ibtidaiyah, 9(1), 27–34. https://doi.org/10.18592/aladzkapgmi.v9i1.3011
Kafai, Y. B., & Burke, Q. (2015). Constructionist gaming: Understanding the benefits of making games for learning. Educational Psychologist, 50(4), 313–334. https://doi.org/10.1080/00461520.2015.1124022
Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2020). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 29(1), 174–188. https://doi.org/10.1007/s10956-019-09798-4
Kong, S. C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers & Education, 151, 103872. https://doi.org/10.1016/j.compedu.2020.103872
Lestari, A. C., & Annizar, A. M. R. (2020). Proses berpikir kritis siswa dalam menyelesaikan masalah PISA ditinjau dari kemampuan berpikir komputasi. Jurnal Kiprah, 8(1), 46-55. https://doi.org/10.31629/kiprah.v8i1.2063
Luo, F., Ijeluola, S. A., Westerlund, J., Walker, A., Denham, A., Walker, J., & Young, C. (2023). Supporting elementary teachers’ technological, pedagogical, and content knowledge in computational thinking integration. Journal of Science Education and Technology, 32(4), 583-596. https://doi.org/10.1007/s10956-023-10045-0
Mladenović, M., Boljat, I., & Zanko, Ž. (2021). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 26(6), 6549–6570. https://doi.org/10.1007/s10639-017-9673-3
Nordine, J., Fortus, D., & Krajcik, J. (2018). Modelling energy transfers between systems to support energy knowledge in use. International Journal of Science Education, 40(15), 1819–1837. https://doi.org/10.1080/03057267.2018.1598048
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
Rahmawati, A., Sudarman, S., & Permana, R. (2020). Science learning innovation with computational thinking approach for elementary school students. Jurnal Pendidikan Sains, 8(1), 54–63. https://doi.org/10.26714/jps.8.1.2020.54-63
Rehmat, A. P., Ehsan, H., & Cardella, M. E. (2020). Instructional strategies to promote computational thinking for young learners. Journal of Digital Learning in Teacher Education, 36(1), 46–62. https://doi.org/10.1080/21532974.2019.1693942
Rich, K. M., Yadav, A., & Schwarz, C. V. (2019). Computational thinking, mathematics, and science: Elementary teachers' perspectives on integration. Journal of Technology and Teacher Education, 27(2), 165–205. https://par.nsf.gov/biblio/10183080
Rosana, D. (2018). Desemination of Authentic Assessment in Local Content-Based Sciences Learning to Achieve The Learning Outcomes Based on Nature of Science. In Journal of Physics: Conference Series (Vol. 1097, No. 1, p. 012034). IOP Publishing. https://doi.org/10.1088/1742-6596/1097/1/012034
Sherwood, H., Yan, W., Liu, R., Martin, W., Adair, A., Fancsali, C., Finzer, W., Fishman, B., Killen, H., Matuk, C., Peek-Brown, D., Sagy, O., Schafer, W., & Israel, M. (2021). Diverse approaches to school-wide computational thinking integration at the elementary grades: A cross-case analysis. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 253–259). Association for Computing Machinery. https://doi.org/10.1145/3408877.3432379
Statter, D., & Armoni, M. (2020). Teaching abstraction in computer science to 7th grade students. ACM Transactions on Computing Education (TOCE), 20(1), 1-37. https://doi.org/10.1145/3372143
Sugiyono. (2018). Metode penelitian pendidikan: Pendekatan kuantitatif, kualitatif, dan R&D [Educational research methods: Quantitative, qualitative, and R&D approaches]. Alfabeta.
Supiarmo, M. G., Sholikin, N. W., Harmonika, S., & Gaffar, A. (2022). Implementasi pembelajaran matematika realistik untuk meningkatkan kemampuan berpikir komputasional siswa. Numeracy, 9(1), 1–13. https://doi.org/10.46244/numeracy.v9i1.1750
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6
Waterman, K. P., Goldsmith, L., & Pasquale, M. (2019). Integrating computational thinking into elementary science curriculum: An examination of activities that support students' computational thinking in the service of disciplinary learning. Journal of Science Education and Technology, 29(1), 53–64. https://doi.org/10.1007/s10956-019-09801-y
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2011). Computational thinking: What and why? The Link Magazine. Carnegie Mellon University. https://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), Article 5. https://doi.org/10.1145/2576872
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