Design and psychometric validation of a metacognitive instrument for physics learning: A focus on heat concepts

Authors

  • Hera Novia Universitas Pendidikan Indonesia https://orcid.org/0000-0003-0851-6450
  • Siska Dewi Aryani Universitas Pendidikan Indonesia
  • Andhy Setiawan Universitas Pendidikan Indonesia
  • Muhammad Zahran Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.58524/jasme.v5i2.818

Keywords:

Metacognitive Knowledge, Physics Education, Instrument Validation, Rasch Model, Metacognitive Questionnaire

Abstract

Background: Metacognitive knowledge, awareness of one's own cognition, tasks, and learning strategies, is critical for independent learning but is often underemphasized in physics education.

Aim: This study aimed to develop and validate a questionnaire to measure high school students’ metacognitive knowledge within the specific context of physics, focusing on the topic of heat.

Method: The research involved content validation by six physics education experts and construct validation through empirical testing with 163 high school students. Content validity was established using Aiken’s V, while construct validity and reliability were evaluated using Rasch model analysis.

Results: The final instrument consisted of 28 items, of which 26 met the Rasch model fit criteria. The analysis confirmed high person reliability (0.82) and item reliability (0.98), indicating strong internal consistency and measurement stability.

Conclusion: The findings support the questionnaire's validity and reliability as a tool for assessing metacognitive knowledge in physics. This validated instrument provides a foundation for future research and instructional practices aimed at enhancing students' metacognitive skills

Author Biographies

  • Hera Novia, Universitas Pendidikan Indonesia
    Program Studi Pendidikan Fisika, FPMIPA
  • Muhammad Zahran, Universitas Pendidikan Indonesia
    Program Studi Pendidikan Fisika, FPMIPA

References

Agnezi, L. A. (2023, September). Development of The Online Assessment Instrument for Fluid, Temperature, and Heat to measure the problem solving skills of high school students. In Journal of Physics: Conference Series (Vol. 2582, No. 1, p. 012053). IOP Publishing. https://doi.org/10.1088/1742-6596/2582/1/012053

Aiken, L. R. (1980). Content validity and reliability of single items or questionnaires. Educational and psychological measurement, 40(4), 955-959. https://doi.org/10.1177/001316448004000419

Beck, B., Peña-Vivas, V., Fleming, S., & Haggard, P. (2019). Metacognition across sensory modalities: Vision, warmth, and nociceptive pain. Cognition, 186, 32-41. https://doi.org/10.1016/j.cognition.2019.01.018

Boone, W. J., & Noltemeyer, A. (2017). Rasch analysis: A primer for school psychology researchers and practitioners. Cogent Education, 4(1), 1416898. https://doi.org/10.1080/2331186X.2017.1416898

Carver, R. P. (1974). Two dimensions of tests: Psychometric and edumetric. American Psychologist, 29(7), 512. https://doi.org/10.1037/h0036782

Chan, S. W., Ismail, Z., & Sumintono, B. (2014). A Rasch model analysis on secondary students’ statistical reasoning ability in descriptive statistics. Procedia-Social and Behavioral Sciences, 129, 133-139. https://doi.org/10.1016/j.sbspro.2014.03.658

Cotterall, S., & Murray, G. (2009). Enhancing metacognitive knowledge: Structure, affordances and self. System, 37(1), 34-45. https://doi.org/10.1016/j.system.2008.08.003

Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated strategies for learning questionnaire (MSLQ) manual. National Center for Research to Improve Postsecondary Teaching and Learning.

Efklides, A., & Vlachopoulos, S. P. (2012). Measurement of metacognitive knowledge of self, task, and strategies in mathematics. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000145

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906. https://doi.org/10.1037/0003-066X.34.10.906

Herdman, M., Fox-Rushby, J., & Badia, X. (1998). A model of equivalence in the cultural adaptation of HRQoL instruments: the universalist approach. Quality of life Research, 7(4), 323-335. https://doi.org/10.1023/A:1008846618880

Hikmah, F. N., Sukarelawan, M. I., Nurjannah, T., & Djumati, J. (2021). Elaboration of high school student’s metacognition awareness on heat and temperature material: Wright map in Rasch model. Indonesian Journal of Science and Mathematics Education, 4(2), 172–182. https://doi.org/10.24042/ijsme.v4i2.9488

Jalil, S., Ali, M. S., & Haris, A. (2018, June). Development and validation of science process skills instrument in physics. In Journal of Physics: Conference Series (Vol. 1028, No. 1, p. 012203). IOP Publishing. https://doi.org/10.1088/1742-6596/1028/1/012203

Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British journal of applied science & technology, 7(4), 396. https://doi.org/10.9734/BJAST/2015/14975

Krathwohl, D. R. (2002). A revision of Bloom's taxonomy: An overview. Theory into practice, 41(4), 212-218. https://doi.org/10.1207/s15430421tip4104_2

Linacre, J. M. (2002). Optimizing rating scale category effectiveness. Journal of applied measurement, 3(1), 85-106.

Molenda, M. (2003). In search of the elusive ADDIE model. Performance improvement, 42(5), 34-37. https://doi.org/10.1002/pfi.4930420508

Novia, H., Kaniawati, I., Rusli, A., & Rusdiana, D. (2019). The development of metacognitive awareness related to the implementation of metacognitive-based learning. In Journal of Physics: Conference Series (Vol. 1170, No. 1, p. 012034). IOP Publishing. https://doi.org/10.1088/1742-6596/1170/1/012034

Peskin, J., & Astington, J. W. (2004). The effects of adding metacognitive language to story texts. Cognitive development, 19(2), 253-273. https://doi.org/10.1016/j.cogdev.2004.01.003

Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory into practice, 41(4), 219-225. https://doi.org/10.1207/s15430421tip4104_3

Radhakrishna, R. B. (2007). Tips for developing and testing questionnaires/instruments. The Journal of Extension, 45(1), 25.

Retnawati, H. (2016). Proving content validity of self-regulated learning scale (The comparison of Aiken index and expanded Gregory index). REiD (Research and Evaluation in Education), 2(2), 4. https://doi.org/10.21831/reid.v2i2.11029

Samsudin, A., Cahyani, P. B., Rusdiana, D., Efendi, R., Aminudin, A. H., & Costu, B. (2021). Development of a Multitier Open-Ended Work and Energy Instrument (MOWEI) Using Rasch Analysis to Identify Students' Misconceptions. Cypriot Journal of Educational Sciences, 16(1), 16-32. https://doi.org/10.18844/cjes.v16i1.5504

Sari, K. M. (2019, June). Metacognitive Knowledge and Critical Thinking Biology 11th of Public Senior High School in Bogor. In Journal of Physics: Conference Series (Vol. 1241, No. 1, p. 012056). IOP Publishing. https://doi.org/10.1088/1742-6596/1241/1/012056

Sukarelawan, M., Sulisworo, D., Kuswanto, H., & Rofiqah, S. A. (2021). Heat and Temperature Metacognition Awareness Inventory: A Confirmatory Factor Analysis. International Journal of Evaluation and Research in Education, 10(2), 389–395. https://doi.org/10.11591/ijere.v10i2.20917

Sumintono, B. (2018, February). Rasch model measurements as tools in assesment for learning. In 1st International Conference on Education Innovation (ICEI 2017) (pp. 38-42). Atlantis Press. https://doi.org/10.2991/icei-17.2018.11

Suryana, T. G. S., Setyadin, A. H., Samsudin, A., & Kaniawati, I. (2020, February). Assessing multidimensional energy literacy of high school students: an analysis of rasch model. In Journal of Physics: Conference Series (Vol. 1467, No. 1, p. 012034). IOP Publishing. https://doi.org/10.1088/1742-6596/1467/1/012034

Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. International journal of academic research in management (IJARM), 5. https://doi.org/10.2139/ssrn.3205040

Teng, M. F. (2025). Metacognition in language teaching. Cambridge University Press.

Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3

Wenden, A. L. (1998). Metacognitive knowledge and language learning. Applied linguistics, 19(4), 515-537. https://doi.org/10.1093/applin/19.4.515

Widhiarso, W., & Sumintono, B. (2016). Examining response aberrance as a cause of outliers in statistical analysis. Personality and Individual Differences, 98, 11-15. https://doi.org/10.1016/j.paid.2016.03.099

Zahran, M., Samsudin, A., Suhandi, A., Aminudin, A. H., Wibowo, F. C., Prahani, B. K., & Kapıcı, H. Ö. (2025). Development of Augmented Reality for Special Education Needs (ARSEN) to Enhance Students' Conceptions of Solar System. Multidisciplinary Science Journal, 7(11), 2025531-2025531. https://doi.org/10.31893/multiscience.2025531

Ziegler, M., & Brunner, M. (2016). Test standards and psychometric modeling. In Psychosocial skills and school systems in the 21st century: Theory, research, and practice (pp. 29-55). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-28606-8_2

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Published

2025-12-03