From Real-world Contexts to Physics Models: Chinese Senior High School Students’ Difficulties in Solving Contextualized Physics Problems

Hao Tai*
Binhai Dongyuan Senior High School, Yancheng 224500, China
*Corresponding email: 516773540@qq.com
https://doi.org/10.71052/grb2025/DQMQ8251

Contextualized physics problems require more than the application of a familiar law: Students must reconstruct a physical system from ordinary language, distributed information, technical devices, and real-world constraints. This qualitative-dominant embedded multiple-case study examined where that reconstruction breaks down among Chinese senior high school students and how classroom and assessment conditions shape the process. The evidence base comprised 8 expert reviews, 12 student cognitive interviews, 24 task-linked student case records selected by school, grade, and performance stratum from a 420-case sampling frame, 15 physics-teacher interviews, and 4 interviews with research or curriculum leaders. Analysis used a recursively connected six-phase framework: context decoding, information structuring, model and idealization, representational translation, solution and monitoring, and interpretation and validation. Four conditions emerged across sources: examination tempo and process compression, contextual-experience fairness, scaffold dependence and fading, and assessment-evidence misalignment. Among the 24 purposefully selected student cases, representational translation was most frequently assigned as the dominant difficulty (10 cases) and remained visible in every performance stratum. Lower-performing cases showed broader cascades across early decoding, model selection, representation, and monitoring; middle-performing cases often knew the relevant law but lost hidden conditions or multi-stage connections. Higher-performing cases generally repaired early breakdowns but still compressed explanation, model boundaries, and transfer. Cross-case analysis produced a recursive mechanism in which early structuring disruption encourages formula-first shortcuts, weakens model-representation coordination, narrows monitoring to arithmetic, and produces premature numerical closure. Examination pace and answer-centered scoring amplify this cycle, whereas phase-specific prompts, explicit representation mapping, model comparison, reasonableness checks, and deliberate scaffold fading provide interruption points. The study contributes a process-based account of contextualized physics difficulty and argues that assessment should capture the evidence chain from situation to model and interpretation rather than reward only the final answer.

References
[1] Wei, Y., Peng, X., Zhong, Y., Pi, F., Zhai, Y., Bao, L. (2025) Can contextualized physics problems enhance student motivation? Physical Review Physics Education Research, 21(2), 020117.
[2] Hallström, J., Norström, P., Schönborn, K. J. (2023) Authentic STEM education through modelling: an international Delphi study. International Journal of STEM Education, 10(1), 62.
[3] Schriebl, D., Müller, A., Robin, N. (2023) Modelling authenticity in science education: modelling authenticity in science education. Science & Education, 32(4), 1021-1048.
[4] Sirnoorkar, A., Bergeron, P. D., Laverty, J. T. (2023) Sensemaking and scientific modeling: intertwined processes analyzed in the context of physics problem solving. Physical Review Physics Education Research, 19(1), 010118.
[5] Ke, L., Schwarz, C. V. (2021) Supporting students’ meaningful engagement in scientific modeling through epistemological messages: a case study of contrasting teaching approaches. Journal of Research in Science Teaching, 58(3), 335-365.
[6] Zhai, X. (2022) Assessing high‐school students’ modeling performance on Newtonian mechanics. Journal of Research in Science Teaching, 59(8), 1313-1353.
[7] Göhner, M. F., Bielik, T., Krell, M. (2022) Investigating the dimensions of modeling competence among preservice science teachers: meta‐modeling knowledge, modeling practice, and modeling product. Journal of Research in Science Teaching, 59(8), 1354-1387.
[8] Zhao, F., Schuchardt, A. (2021) Development of the sci-math sensemaking framework: categorizing sensemaking of mathematical equations in science. International Journal of STEM Education, 8(1), 10.
[9] Kaldaras, L., Wieman, C. (2023) Cognitive framework for blended mathematical sensemaking in science. International Journal of STEM Education, 10(1), 18.
[10] Wang, Z., Cheng, H., Qin, Q. (2025) The research on video analysis of key motion positions based on deep learning technology. International Journal of e-Collaboration (IJeC), 21(1), 1-16.
[11] Ibrahim, B., Ding, L. (2021) Sequential and simultaneous synthesis problem solving: a comparison of students’ gaze transitions. Physical Review Physics Education Research, 17(1), 010126.
[12] Susac, A., Planinic, M., Bubic, A., Jelicic, K., Palmovic, M. (2023) Effect of representation format on conceptual question performance and eye-tracking measures. Physical Review Physics Education Research, 19(2), 020114.
[13] Hahn, L., Klein, P. (2022) Eye tracking in physics education research: a systematic literature review. Physical Review Physics Education Research, 18(1), 013102.
[14] Tong, T., Pi, F., Zheng, S., Zhong, Y., Lin, X., Wei, Y. (2025) Exploring the effect of mathematics skills on student performance in physics problem-solving: a structural equation modeling analysis. Research in Science Education, 55(3), 489-509.
[15] Bowers, J., Anderson, M., Beckhard, K. (2024) A mathematics educator walks into a physics class: Identifying math skills in students’ physics problem-solving practices. Journal for STEM Education Research, 7(3), 335-361.
[16] Shi, F., Wang, L., Liu, X., Chiu, M. H. (2021) Development and validation of an observation protocol for measuring science teachers’ modeling‐based teaching performance. Journal of Research in Science Teaching, 58(9), 1359-1388.
[17] Böschl, F., Forbes, C., Lange‐Schubert, K. (2023) Investigating scientific modeling practices in US and German elementary science classrooms: a comparative, cross‐national video study. Science Education, 107(2), 368-400.
[18] Magana, A. J., Arigye, J., Udosen, A., Lyon, J. A., Joshi, P., Pienaar, E. (2024) Scaffolded team-based computational modeling and simulation projects for promoting representational competence and regulatory skills. International Journal of STEM Education, 11(1), 34.
[19] Webb, D. J., Paul, C. A. (2023) Attributing equity gaps to course structure in introductory physics. Physical Review Physics Education Research, 19(2), 020126.
[20] Sijmkens, E., De Laet, T., De Cock, M. (2026) Understanding and enhancing students’ use of evaluation strategies during physics problem solving through reflective prompts. Physical Review Physics Education Research, 22(2), 020101.
[21] Wu, C. J., Liu, C. Y. (2021) Eye-movement study of high-and low-prior-knowledge students’ scientific argumentations with multiple representations. Physical Review Physics Education Research, 17(1), 010125.
[22] Hallström, J., Schönborn, K. J. (2019) Models and modelling for authentic STEM education: reinforcing the argument. International Journal of STEM education, 6(1), 22.

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Tai, H. (2026) From Real-world Contexts to Physics Models: Chinese Senior High School Students’ Difficulties in Solving Contextualized Physics Problems. Global Education Bulletin, 3(3), 64-80. https://doi.org/10.71052/grb2025/DQMQ8251

Published

11/08/2026