Analysis of Students’ Difficulties in Applying Computational Thinking in Numerical Physics Learning

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DOI:

10.29303/jpft.v12i2.12187

Published:

2026-07-30

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Abstract

Computational thinking skills are essential for numerical physics learning; however, students’ mastery of these skills remains considerably below expectations. This study aims to analyze the difficulties experienced by Physics Education students in applying computational thinking to numerical physics learning. A survey research design employing a quantitative descriptive approach was used. Data were collected through a 15-item questionnaire administered via Google Forms to 31 students from the 2022 and 2023 cohorts of the Physics Education program, selected using purposive sampling. The questionnaire demonstrated good reliability, with a Cronbach’s alpha coefficient of 0.82, while its content validity was established through expert judgment. Descriptive statistical analysis revealed an overall mean difficulty score of 3.68 on a five-point scale, indicating a relatively high level of difficulty. The highest level of difficulty was found in the conceptual understanding aspect (M = 3.72), particularly in integrating physics theory with computational implementation, whereas the practical application aspect showed a slightly lower mean score (M = 3.63). These findings indicate that students face substantial challenges in both understanding and applying computational thinking in numerical physics contexts. The study provides an empirical basis for improving instruction, particularly through the development of curricula and teaching strategies that more effectively support students’ computational thinking competencies in physics education

Keywords:

Computational Thingking Skills Numerical Physics Learning Aspect of Computational Thingking Physics Education Student

References

Ahmad, M., & Wilkins, S. (2025). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity, 59(2), 1461–1479. https://doi.org/10.1007/s11135-024-02022-5

Ayasrah, M. N., Gharaibeh, M., Khatib, A. J. A., Khasawneh, M. A. S., & Aboutaleb, A. H. (2026). Exploring executive function-based learning challenges in AI-enhanced education: development and validation of a novel psychometric scale using network and machine learning analysis. Current Psychology, 45(8), 855–861. https://doi.org/10.1007/s12144-026-09404-y

Bai, Y., Li, G., & Bao, L. (2026). Knowledge integration in student learning of the ideal gas law. Physical Review Physics Education Research, 22(1), 101–112. https://doi.org/10.1103/vv1g-7yl8

Belmar, H. (2022). Review of the teaching of programming and computational thinking in the world. Frontiers in Computer Science, 4(1), 997–1014. https://doi.org/10.3389/fcomp.2022.997222

Dasu, G., O'Rourke, E., Zhang, H., Whitely, M., Wilcox, R., Rosenberg, L., & Brewster, N. (2026). Process management for learning from professional source code: Cultivating experts in the age of AI. ACM Transactions on Computing Education, 26(3), 1–83. https://doi.org/10.1145/3796525

de Oliveira, A. M., Vaz-Rebelo, P., & Bidarra, M. D. G. (2025). Unplugged activities in the development of computational thinking with poly-universe. Multimodal Technologies and Interaction, 9(9), 95–107. https://doi.org/10.3390/mti9090095

DiSessa, A. A. (2018). Computational literacy and "the big picture" concerning computers in mathematics education. Mathematical Thinking and Learning, 20(1), 3–31. https://doi.org/10.1080/10986065.2018.1403544

Govindasamy, P., Cumming, T. M., & Abdullah, N. (2024). Validity and reliability of a needs analysis questionnaire for the development of a creativity module. Journal of Research in Special Educational Needs, 24(3), 637–652. https://doi.org/10.1111/1471-3802.12659

Hamerski, P. C., McPadden, D., Caballero, M. D., & Irving, P. W. (2022). Students' perspectives on computational challenges in physics class. Physical Review Physics Education Research, 18(2), 201–211. https://doi.org/10.1103/PhysRevPhysEducRes.18.020109

Hundhausen, C. D., Agrawal, A., & Agarwal, P. (2013). Talking about code: Integrating pedagogical code reviews into early computing courses. ACM Transactions on Computing Education (TOCE), 13(3), 1–28. https://doi.org/10.1145/2499947.2499951

Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., & McElhaney, K. (2020). C2STEM: A system for synergistic learning of physics and computational thinking. Journal of Science Education and Technology, 29(1), 83–100. https://doi.org/10.1007/s10956-019-09804-9

Juškevičienė, A., & DagienĖ, V. (2018). Computational thinking and digital competence. Informatics in Education, 17(2), 265-284. https://doi.org/10.15388/infedu.2018.14

Kantaros, A., Ganetsos, T., Pallis, E., & Papoutsidakis, M. (2025). From mathematical modeling and simulation to digital twins: Bridging theory and digital realities in industry and emerging technologies. Applied Sciences, 15(16), 9213–9221. https://doi.org/10.3390/app15169213

Kieser, F., Wulff, P., Kuhn, J., & Küchemann, S. (2023). Educational data augmentation in physics education research using ChatGPT. Physical Review Physics Education Research, 19(2), 201–213. https://doi.org/10.1103/PhysRevPhysEducRes.19.020150

Kotsis, K. T. (2025). Artificial intelligence for physics education in STEM classrooms: A narrative review within a pedagogy technology policy framework. Schrödinger: Journal of Physics Education, 6(3), 204–211. https://doi.org/10.37251/sjpe.v6i3.2148

Koudsia, S., & Kirchner, M. (2024). Reducing cognitive overload for students in higher education: A course design case study. Journal of Higher Education Theory and Practice, 24(10), 97–124. https://doi.org/10.33423/jhetp.v24i10.7382

Lee, I., & Malyn-Smith, J. (2020). Computational thinking integration patterns along the framework, defining computational thinking from a disciplinary perspective. Journal of Science Education and Technology, 29(1), 9–18. https://doi.org/10.1007/s10956-019-09802-x

Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1–8. https://doi.org/10.1007/s10956-019-09803-w

Lindberg, A. (2020). Developing theory through integrating human and machine pattern recognition. Journal of the Association for Information Systems, 21(1), 7–18. https://doi.org/10.17705/1jais.00593

Lund, B. (2023). The questionnaire method in systems research: an overview of sample sizes, response rates, and statistical approaches utilized in studies. VINE Journal of Information and Knowledge Management Systems, 53(1), 1–10. https://doi.org/10.1108/VJIKMS-08-2020-0156

Magana, A. J., Falk, M. L., Vieira, C., Reese Jr, M. J., Alabi, O., & Patinet, S. (2017). Affordances and challenges of computational tools for supporting modeling and simulation practices. Computer Applications in Engineering Education, 25(3), 352–375. https://doi.org/10.1002/cae.21804

Martin, P. P., & Graulich, N. (2023). When a machine detects student reasoning: a review of machine learning-based formative assessment of mechanistic reasoning. Chemistry Education Research and Practice, 24(2), 407–427. https://doi.org/10.1039/D2RP00287F

Mills, K. A., Cope, J., Scholes, L., & Rowe, L. (2025). Coding and computational thinking across the curriculum: A review of educational outcomes. Review of Educational Research, 95(3), 581–618. https://doi.org/10.3102/00346543241241327

Mills, M. S., & Watson, J. H. (2021). Breaking free: The role of psychological safety and productive failure in creative pathmaking. TechTrends, 65(4), 668–676. https://doi.org/10.1007/s11528-021-00620-w

Munasinghe, B., Bell, T., & Robins, A. (2023). Computational thinking and notional machines: The missing link. ACM Transactions on Computing Education, 23(4), 1–27. https://doi.org/10.1145/3627829

Odden, T. O. B., Lockwood, E., & Caballero, M. D. (2019). Physics computational literacy: An exploratory case study using computational essays. Physical Review Physics Education Research, 15(2), 201–213. https://doi.org/10.1103/PhysRevPhysEducRes.15.020152

Pakala, Y., & Guniganti, S. (2026). Advancing digital extension education: Development and validation of digital learning engagement scale. Indian Journal of Extension Education, 62(1), 159–165. https://doi.org/10.48165/IJEE.2026.621RT04

Poláková, M., Suleimanová, J. H., Madzík, P., Copuš, L., Molnárová, I., & Polednová, J. (2023). Soft skills and their importance in the labour market under the conditions of Industry 5.0. Heliyon, 9(8), 798–1015. https://doi.org/10.1016/j.heliyon.2023.e18670

Qian, Y., & Choi, I. (2023). Tracing the essence: ways to develop abstraction in computational thinking. Educational Technology Research and Development, 71(3), 1055–1078. https://doi.org/10.1007/s11423-022-10182-0

Qian, Y., & Lehman, J. (2017). Students' misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education (TOCE), 18(1), 1–24. https://doi.org/10.1145/3077618

Rao, T. S. S., & Bhagat, K. K. (2024). Computational thinking for the digital age: a systematic review of tools, pedagogical strategies, and assessment practices. Educational Technology Research & Development, 72(4), 1893-1908. https://doi.org/10.1007/s11423-024-10364-y

Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. https://doi.org/10.1007/s10639-012-9240-x

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22(1), 142–158. https://doi.org/10.1016/j.edurev.2017.09.003

Szabo, Z. K., Körtesi, P., Guncaga, J., Szabo, D., & Neag, R. (2020). Examples of problem-solving strategies in mathematics education supporting the sustainability of 21st-century skills. Sustainability, 12(23), 101–114. https://doi.org/10.3390/su122310113

Tekdal, M. (2021). Trends and development in research on computational thinking. Education and Information Technologies, 26(5), 6499–6529. https://doi.org/10.1007/s10639-021-10617-w

Tofel-Grehl, C., Searle, K. A., & Ball, D. (2022). Thinking through making: Mapping computational thinking practices onto scientific reasoning. Journal of Science Education and Technology, 31(6), 730–746. https://doi.org/10.1007/s10956-022-09989-6

Tucker, S. Y. (2014). Transforming pedagogies: Integrating 21st century skills and Web 2.0 technology. Turkish Online Journal of Distance Education, 15(1), 166–173. https://doi.org/10.17718/tojde.32300

Vassallo, D. (2025). Fostering computational thinking in early learners: an iterative approach in a Maltese primary school. Discover Education, 4(1), 126–134. https://doi.org/10.1007/s44217-025-00553-z

Vedder-Weiss, D., Ehrenfeld, N., Ram-Menashe, M., & Pollak, I. (2018). Productive framing of pedagogical failure: How teacher framings can facilitate or impede learning from problems of practice. Thinking Skills and Creativity, 30(1), 31–41. https://doi.org/10.1016/j.tsc.2018.01.002

Vieyra, R., & Himmelsbach, J. (2022). Teachers' disciplinary boundedness in implementing integrated computational modeling in physics. Journal of Science Education and Technology, 31(2), 153–165. https://doi.org/10.1007/s10956-021-09938-9

Wang, C., Shen, J., & Chao, J. (2022). Integrating computational thinking in STEM education: A literature review. International Journal of Science and Mathematics Education, 20(8), 1949-1972. https://doi.org/10.1007/s10763-021-10227-5

Wang, H., Yan, H., Rong, C., Yuan, Y., Jiang, F., Han, Z., & Li, Y. (2024). Multi-scale simulation of complex systems: a perspective of integrating knowledge and data. ACM Computing Surveys, 56(12), 1–38. https://doi.org/10.1145/3654662

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st-century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568. https://doi.org/10.1007/s11528-016-0087-7

Zhu, L., Chen, W., Liu, J., Jiang, M., & Yang, Y. (2026). Algorithmic explanations as a scaffold for scientific explanation: A quasi-experimental and time-series study in high school chemistry. Research in Science Education, 1(1), 1–36. https://doi.org/10.1007/s11165-026-10353-6

Author Biographies

Wylla Agustiningrum, UIN Sunan Kalijaga Yogyakarta

Author Origin : Indonesia

Physics Education Program

Himawan Putranta, UIN Sunan Kalijaga Yogyakarta

Author Origin : Indonesia

Physics Education Program

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How to Cite

Agustiningrum, W., & Putranta, H. (2026). Analysis of Students’ Difficulties in Applying Computational Thinking in Numerical Physics Learning . Jurnal Pendidikan Fisika Dan Teknologi, 12(2), 366–381. https://doi.org/10.29303/jpft.v12i2.12187