Unraveling Scientific Creativity in Fluid Dynamics: An Item Response Theory Approach with Rasch Analysis for High School Learners

Authors

DOI:

https://doi.org/10.30998/formatif.v16i2.3841

Keywords:

Scientific creativity; Fluid dynamic; Item Response Theory; Rasch model

Abstract

Scientific creativity is a key 21st-century skill that should be assessed to identify students' creative potential. This is important for mapping out and guiding students toward the fields of interest they will pursue in the future. However, the measurement of scientific creativity in schools is still dominated by conventional ordinal score-based approaches, which fail to clearly reflect individual students’ abilities. This indicates a gap between modern assessment expectations and limited evaluation practices. This study aimed to develop the Scientific Creativity Test in Fluid Dynamics (SCTDF) for high-school students and to conduct a preliminary psychometric evaluation of its measurement functioning using the Rasch model. This study used the 4D Research and Development model and involved 50 high school students in Pamekasan Regency, East Java. The research instrument was a test consisting of 14 multiple-choice items based on seven indicators of scientific creativity and fluid dynamics. Data analysis was conducted using the Ministep application according to the Rasch Model criteria. The findings provide preliminary Rasch-based evidence regarding the measurement functioning of the SCTDF. Most items demonstrated acceptable model–data fit; however, one item showed misfit and person reliability was low (0.49), indicating limited precision in differentiating respondents along the measured continuum. Further item refinement and validation with larger and more diverse samples are therefore required. The study concludes that the SCTDF can serve as an alternative tool for assessing scientific creativity based on modern psychometrics, although revisions to certain items are still required to improve measurement accuracy

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References

Akbari, A. (2025). The Rasch Analysis of Item Response Theory: An Untouched Area in Evaluating Student Academic Translations. SKASE Journal of Translation and Interpretation, 18(1). https://doi.org/10.33542/JTI2025-1-05

Aleksic, V., Milic, M., Dotlic, J., Jeremic, B., Djerkovic, B., Radic, I., Karanovic, A., Kulic, L., Galjak, M., & Gazibara, T. (2024). The Night Eating Questionnaire through the lens of the Rasch model. Heliyon, 10(11), e31929. https://doi.org/10.1016/j.heliyon.2024.e31929

Chan, S.-W., Looi, C.-K., & Sumintono, B. (2021). Assessing computational thinking abilities among Singapore secondary students: a Rasch model measurement analysis. Journal of Computers in Education, 8(2), 213–236. https://doi.org/10.1007/s40692-020-00177-2

Creswell, J. W. (2014). Reseach Design: Pendekatan, Kualitatif, Kuantitatif, dan Mixed. Pustaka pelajar.

Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555

Dilekçi, A., & Karatay, H. (2023). The effects of the 21st century skills curriculum on the development of students’ creative thinking skills. Thinking Skills and Creativity, 47(Marc 2023), 101229. https://doi.org/https://doi.org/10.1016/j.tsc.2022.101229

Engelhard, G., & Wind, S. A. (2026). Evaluating Measurement Quality for Dichotomous Rasch Models. In Invariant Measurement with Explanatory Rasch Models in the Human Sciences (pp. 63–92). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-08816-1_4

Firdaus, F., Wiyanto, W., Putra, N. M. D., & Isnaeni, W. (2025). Design of instruments for scientific creative thinking skills and creative thinking digital skills: Rasch models and confirmatory factor analysis. Eurasia Journal of Mathematics, Science and Technology Education, 21(5), em2632. https://doi.org/10.29333/ejmste/16310

Gittel, L., Li, J. Q., Dell, J., Wintergerst, M. W. M., Heinz, C., Finger, R. P., & Terheyden, J. H. (2024). Piloting Rasch model scoring of the National Eye Institute Visual Function Questionnaire in uveitis. Journal of Ophthalmic Inflammation and Infection, 14(1), 16. https://doi.org/10.1186/s12348-024-00398-x

Hikmah, N., Herayanti, L., & Gummah, S. (2025). Development of Ethnoscience-Based Teaching Materials on Fluid Dynamics to Enhance Students ’ Creativity. Jurnal Pendidikan Fisika Dan Teknologi (JPFT), 11(1). https://doi.org/https://dx.doi.org/10.29303/jpft.v11i1a.9575

Hong, M., Lin, L., & Cheng, Y. (2021). Asymptotically Corrected Person Fit Statistics for Multidimensional Constructs with Simple Structure and Mixed Item Types. Psychometrika, 86(2), 464–488. https://doi.org/10.1007/s11336-021-09756-3

Hope, D., Kluth, D., Homer, M., Dewar, A., Goddard-Fuller, R., Jaap, A., & Cameron, H. (2025). Exploring the use of Rasch modelling in “common content” items for multi-site and multi-year assessment. Advances in Health Sciences Education, 30(2), 427–438. https://doi.org/10.1007/s10459-024-10354-y

Hu, W., & Adey, P. (2002). A scientific creativity test for secondary school Student. Research Report, 24(4), 389–403. https://doi.org/10.1080/09500690110098912

Irey, R., Blum, A. M., Carpenter, E., Miller, Z., Vonk, J. M. J., & Gorno‐Tempini, M. L. (2026). Backward Construct Mapping in Practice: Using Rasch Measurement to Reveal the Internal Structure of Inflectional Morphological Processing in Students with Dyslexia. Journal of Educational Measurement, 63(2). https://doi.org/10.1111/jedm.70047

Iriyadi, D., Asdar, A. K., Afriadi, B., Samad, M. A., & Syaputra, Y. D. (2024). Analysis of the Indonesian Version of the Statistical Anxiety Scale (SAS) Instrument with a Psychometric Approach. International Journal of Evaluation and Research in Education (IJERE), 13(2), 672. https://doi.org/10.11591/ijere.v13i2.26517

Irma, Z. U., Kusairi, S., & Yuliati, L. (2023). Strem Pbl With E-Authentic Assessment: Its Impact To Students’ Scientific Creativity on Static Fluid. Jurnal Pendidikan IPA Indonesia, 12(1), 80–95. https://doi.org/10.15294/jpii.v12i1.40214

Jin, K.-Y., & Eckes, T. (2025). When raters generalize: Examining sources of halo effects with mixture Rasch facets models. Behavior Research Methods, 57(5), 149. https://doi.org/10.3758/s13428-025-02667-6

Julianto, V., Sumintono, B., Almakhi, N. P. Z., Avetazain, H., Wilhelmina, T. M., & Wati, D. A. (2025). Academic Motivation Scale’s psychometric attribute: analysis using Rasch measurement model. Current Psychology, 44(1), 114–124. https://doi.org/10.1007/s12144-024-07142-7

Kangwanrattanakul, K., & Kulthanachairojana, N. (2024). Modern psychometric evaluation of Thai WHOQOL-BREF and its shorter versions in patients undergoing warfarin in Thailand: Rasch analysis. Scientific Reports, 14(1), 20639. https://doi.org/10.1038/s41598-024-71048-4

Kiefer, C., & Sengewald, M.-A. (2025). Mining exceptional Rasch models. Behaviormetrika, 52(2), 361–391. https://doi.org/10.1007/s41237-024-00251-4

Leung, K., Trevena, L., & Waters, D. (2018). Content validation of the evidence-based nursing practice assessment tool. Nurse Researcher, 26(1), 33–40. https://doi.org/10.7748/nr.2018.e1544

Li, Y., Zhang, J., Sheng, Y., Yao, J., Zhang, X., & Xu, X. (2025). Rasch analysis and application research of the Chinese version of the Skidmore anxiety stigma scale: a cross-sectional study. Scientific Reports, 15(1), 41412. https://doi.org/10.1038/s41598-025-25301-z

Liu, F., Qu, S., Fan, Y., Chen, F., & He, B. (2023). Scientific creativity and innovation ability and its determinants among medical postgraduate students in Fujian province of China: a cross sectional study. BMC Medical Education, 23(1), 1–13. https://doi.org/10.1186/s12909-023-04408-9

Liu, Q., Wald, N., Daskon, C., & Harland, T. (2024). Multiple-choice questions (MCQs) for higher-order cognition: Perspectives of university teachers. Innovations in Education and Teaching International, 61(4), 802–814. https://doi.org/10.1080/14703297.2023.2222715

Lu, J., Wang, X., & Zhang, J. (2025). Joint modeling with generalized item response theory model family and response time model: Enhancing model structural flexibility and data-fitting adequacy. Behavior Research Methods, 57(12), 330. https://doi.org/10.3758/s13428-025-02855-4

Naidoo, M. (2023). Twenty-First Century Learning (21 CL) - South African Private Secondary Schools in KwaZulu-Natal. International Journal of Learning, Teaching and Educational Research, 22(5), 26–57. https://doi.org/10.26803/ijlter.22.5.2

Panadero, E., Jonsson, A., Pinedo, L., & Fernández-Castilla, B. (2023). Effects of Rubrics on Academic Performance, Self-Regulated Learning, and self-Efficacy: a Meta-analytic Review. Educational Psychology Review, 35(4), 113. https://doi.org/10.1007/s10648-023-09823-4

Pelouto, F., Papri, N., Haagsma, J. A., Cornblath, D. R., Nobile-Orazio, E., Wiegers, E. J. A., Harbo, T., Rajabally, Y. A., van Doorn, P. A., Gorson, K. C., Terwee, C. B., Jacobs, B. C., Horton, M. C., Antonini, G., Arends, S., Attarian, S., Barroso, F. A., Bateman, K., Benedetti, L., … van Woerkom, M. (2026). Measurement properties of the Inflammatory Rasch-built Overall Disability Scale (I-RODS) in patients with Guillain–Barré syndrome. Journal of Neurology, 273(2), 164. https://doi.org/10.1007/s00415-026-13684-6

Pinar, F. I. L., Panergayo, A. A. E., Sagcal, R. R., Acut, D. P., Roleda, L. S., & Prudente, M. S. (2025). Fostering scientific creativity in science education through scientific problem-solving approaches and STEM contexts: a meta-analysis. Disciplinary and Interdisciplinary Science Education Research, 7(1). https://doi.org/10.1186/s43031-025-00137-9

Prahani, B. K., Suprapto, N., Rachmadiarti, F., Sholahuddin, A., Mahtari, S., Suyidno, & Siswanto, J. (2021). Online Scientific Creativity Learning (OSCL) in Science Education to Improve Students’ Scientific Creativity in Covid-19 Pandemic. Journal of Turkish Science Education, 18(Special Issue), 77–90. https://doi.org/10.36681/tused.2021.73

Shaw, A., Kapnek, M., & Morelli, N. (2021). Measuring Creative Self-Efficacy: An Item Response Theory Analysis of the Creative Self-Efficacy Scale. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.678033

Sumo, M., & Soeharto, S. (2025). Fostering Scientific Creativity Through Feasibility Project-Based Learning in Undergraduate Physics. J.Sci.Learn.2025, 8(2), 191–205. https://doi.org/10.17509/jsl.v8i2.83225

Touzani, R., Rouquette, A., Schultz, E., Allaire, C., Carrieri, P., Mancini, J., & Hardouin, J.-B. (2024). Psychometric validation of the French version of two scales measuring general (HLS19-Q12) and navigational (HLS19-NAV) health literacy using the Rasch model. BMC Public Health, 24(1), 3079. https://doi.org/10.1186/s12889-024-20504-x

Tsai, Y. J., Chen, C. C., Hsu, Y. Y., & Chen, C. C. (2025). Validation of the uncertainty stress scale-high-risk pregnancy - Chinese brief version: Rasch analysis. BMC Pregnancy and Childbirth, 25(1), 6. https://doi.org/10.1186/s12884-024-07078-7

Tutz, G. (2025). The Binary Rasch Model (pp. 9–35). https://doi.org/10.1007/978-3-031-87271-6_2

Uba, A. R., & Khairani, A. Z. (2024). Assessing Item Reliability, Differential Item Functioning, and Wright Map Analysis of the GSP122 Test at a Public University in Nigeria. Journal of Education For Sustainable Innovation, 2(2), 107–120. https://doi.org/10.56916/jesi.v2i2.934

Xu, S., Reiss, M. J., & Lodge, W. (2024). Comprehensive Scientific Creativity Assessment (C-SCA): A New Approach for Measuring Scientific Creativity in Secondary School Students. International Journal of Science and Mathematics Education, 23(2), 293–319. https://doi.org/10.1007/s10763-024-10469-z

Xu, S., Reiss, M. J., & Lodge, W. (2025). Comprehensive Scientific Creativity Assessment (C-SCA): A New Approach for Measuring Scientific Creativity in Secondary School Students. International Journal of Science and Mathematics Education, 23(2), 293–319. https://doi.org/10.1007/s10763-024-10469-z

Yuanyuan, P., Alias, B. S., Mansor, A. N., & Hamid, M. R. A. (2025). Development and validation of the principals’ digital leadership instrument using Rasch measurement model. International Journal of Evaluation and Research in Education (IJERE), 14(3), 1577. https://doi.org/10.11591/ijere.v14i3.32214

Zaky Adhitya Ginanjar Sasmita, Wahono Widodo, & Sifak Indana. (2021). Contextual Based Learning Media Development to Train Creative Thinking Skill in Primary School. IJORER : International Journal of Recent Educational Research, 2(4), 468–476. https://doi.org/10.46245/ijorer.v2i4.124

Zhao, Y., Luo, Y., Miao, Y., Li, J., Hou, Q., Dallakoti, N., & Wu, Y. (2025). Rasch analysis of the mobile version of the eHealth literacy scale for cardiovascular disease patients. Scientific Reports, 15(1), 34093. https://doi.org/10.1038/s41598-025-14198-3

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Published

2026-09-30

How to Cite

Sumo, M., Oetpah, F., & Pagal, E. D. (2026). Unraveling Scientific Creativity in Fluid Dynamics: An Item Response Theory Approach with Rasch Analysis for High School Learners. Formatif : Jurnal Ilmiah Pendidikan MIPA, 16(2), 397-410. https://doi.org/10.30998/formatif.v16i2.3841