Validity Analysis of VR-Based Particle Dynamics Module Development Using the Rasch Model

Authors

DOI:

https://doi.org/10.58524/oler.v5i2.803

Keywords:

Module Development, Particle Dynamics, Rasch Model, Virtual Reality (VR)

Abstract

Physics education often encounters challenges in fostering students’ conceptual understanding of particle dynamics, largely due to the abstract nature of the content and its reliance on symbolic mathematical representations, which can impede meaningful interpretation of physical phenomena. To address this issue, this study developed PADYVIR (Particle Dynamics Virtual Reality), a virtual reality–assisted learning module, and examined its validity, reliability, and practicality for use in higher education settings. The development process followed the ADDIE instructional design model, after which the module underwent expert appraisal and psychometric evaluation using the Rasch measurement model. Expert assessments involved physics education specialists and instructional design experts, who evaluated the content, construct clarity, and pedagogical appropriateness of PADYVIR and its supporting learning instruments. The research instruments used in this study were adapted from previously validated tools reported in the literature, thereby ensuring the validity of baseline measurements. Rasch analysis indicated strong psychometric properties, including high person and item reliability indices, acceptable fit statistics, and evidence of unidimensionality. A limited trial with undergraduate students further demonstrated the high practicality of the system in terms of usability, attractiveness, and comprehensibility. These findings confirm that PADYVIR is a psychometrically robust and user-friendly instructional resource capable of supporting the visualisation and conceptual learning of particle dynamics. Methodologically, the study contributes to the field by integrating rigorous measurement theory with immersive educational technology, offering a replicable framework for the development and evaluation of virtual reality–based learning media. Implications for instructional design and educational technology research are discussed, and directions for future effectiveness studies are proposed

References

Abdikadyr, B., Ualikhanova, B., Berdaliyev, D., Issayeva, G., & Maxutov, S. (2025). Reducing gender gaps in physics achievement: The role of constructivist methods. European Journal of Science and Mathematics Education, 13(2), 58–76. https://doi.org/10.30935/scimath/16037

Amini, R., & Usmeldi. (2020). The development of teaching materials using an inductive-based 7E learning cycle for elementary school students. Journal of Physics: Conference Series, 1521(4), 042114. https://doi.org/10.1088/1742-6596/1521/4/042114

Aryadoust, V., Tan, H. A. H., & Ng, L. Y. (2019). A scientometric review of Rasch measurement: The rise and progress of a specialty. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02197

Asih, F., Poedjiastoeti, S., Lutfi, A., Novita, D., Ismono, I., & Purnamasari, A. (2022). The practicality and effectiveness of a case study-based module on chemical thermodynamics (ideal and real gases) during the COVID-19 pandemic. Journal of Technology and Science Education, 12(2), 466. https://doi.org/10.3926/jotse.1654

Asrizal, Desnita, & Darvina, Y. (2021). Analysis of validity and practicality tests of a physics enrichment e-book based on CTL and environmental factors. Journal of Physics: Conference Series, 1876(1), 012034. https://doi.org/10.1088/1742-6596/1876/1/012034

Ayu, H. D., Chusniyah, D. A., Kurniawati, M. P., Purwanti, P. F., Lukitawanti, S. D., & Putri, A. N. (2024). Problem-based learning as an effective solution to enhance understanding of physics concepts: Systematic literature review. Journal of Environment and Sustainability Education, 2(2). https://doi.org/10.62672/joease.v2i2.29

Ayu, H. D., Saputro, S., Sarwanto, & Mulyani, S. (2023). Reshaping technology-based projects and their exploration of creativity. Eurasia Journal of Mathematics, Science and Technology Education, 19(1). https://doi.org/10.29333/ejmste/12814

Azizah, A., & Wahyuningsih, S. (2020). Penggunaan model Rasch untuk analisis instrumen tes pada mata kuliah matematika aktuaria. JUPITEK: Jurnal Pendidikan Matematika, 3(1), 45–50. https://doi.org/10.30598/jupitekvol3iss1pp45-50

Bond, T. G., Yan, Z., & Heene, M. (2021). Applying the Rasch model: Fundamental measurement in the human sciences. Routledge.

Boone, W. J. (2016). Rasch analysis for instrument development: Why, when, and how? CBE—Life Sciences Education, 15(4), rm4. https://doi.org/10.1187/cbe.16-04-0148

Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer.

Brundage, M. J., Meltzer, D. E., & Singh, C. (2024). Investigating introductory and advanced students’ difficulties with entropy and the second law of thermodynamics using a validated instrument. Physical Review Physics Education Research, 20(2), 020110. https://doi.org/10.1103/PhysRevPhysEducRes.20.020110

Cloete, R., Norval, C., & Singh, J. (2021). Auditable augmented/mixed/virtual reality. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(4), 1–24. https://doi.org/10.1145/3495001

Dahal, N., Pant, B. P., Luitel, B. C., Khadka, J., Shrestha, I. M., Manandhar, N. K., & Rajbanshi, R. (2023). Development and evaluation of E-learning courses. International Journal of Interactive Mobile Technologies, 17(12), 40–60. https://doi.org/10.3991/ijim.v17i12.40317

Damaševičius, R., & Sidekerskienė, T. (2024). Virtual worlds for learning in metaverse: A narrative review. Sustainability, 16(5). https://doi.org/10.3390/su16052032

Defianti, A., & Rohmi, P. (2021). Undergraduate students’ misconceptions about projectile motion after learning physics during the COVID-19 pandemic era. Journal of Physics: Conference Series, 2098(1), 012026. https://doi.org/10.1088/1742-6596/2098/1/012026

Demirezen, M. U., Yilmaz, O., & Ince, E. (2023). New models developed for detection of misconceptions in physics with artificial intelligence. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08414-2

Dewi, I. P., Ambiyar, Mursyida, L., Effendi, H., Giatman, M., Efrizon, Hanafi, H. F., & Ali, S. K. (2024). Virtual reality in algorithm programming course: Practicality and implications for college students. International Journal on Informatics Visualization, 8(3–2), 1720. https://doi.org/10.62527/joiv.8.3-2.3113

Erceg, N., Aviani, I., Grlaš, K., Karuza, M., & Mešić, V. (2019). Development of the kinetic molecular theory of liquids concept inventory: Preliminary results on university students’ misconceptions. European Journal of Physics, 40(2), 025704. https://doi.org/10.1088/1361-6404/aaff36

Fischer, H. E., & Girwidz, R. (Eds.). (2022). Physics education. Springer. https://doi.org/10.1007/978-3-030-87391-2

Fisher, A. G. (1993). The assessment of IADL motor skills: An application of many-faceted Rasch analysis. American Journal of Occupational Therapy, 47(4), 319–329. https://doi.org/10.5014/ajot.47.4.319

García Trillos, C. A., & García Trillos, N. (2024). On adversarial robustness and the use of Wasserstein ascent–descent dynamics to enforce it. Information and Inference, 13(3). https://doi.org/10.1093/imaiai/iaae018

González, M. L. C. (2017). Techno-pedagogical models for teacher training in higher education. In Research on university teaching and faculty development: International perspectives (pp. 389–408). Springer.

Gunawan, K. D. H., Liliasari, Kaniawati, I., Setiawan, W., Rochintaniawati, D., & Sinaga, P. (2021). Profile of teachers’ integrated science curricula supported by intelligent tutoring systems. Journal of Physics: Conference Series, 1806(1), 012139. https://doi.org/10.1088/1742-6596/1806/1/012139

Gunawan, K. D. H., Liliasari, S., Kaniawati, I., & Setiawan, W. (2020). Exploring science teachers’ lesson plans through intelligent tutoring systems in blended learning environments. Universal Journal of Educational Research, 8(10), 4776–4783. https://doi.org/10.13189/ujer.2020.081049

Gunawan, K. D. H., Utami, B., Bramastia, B., Suciati, S., Hudha, M. N., Ridhani, J., & Adimudra, D. A. S. (2025). Innovating IoT instruction through simulation-based modules: An R&D study in higher education. Momentum: Physics Education Journal, 9(2), 374–385. https://doi.org/10.21067/mpej.v9i2.12897

Hagquist, C. (2008). Psychometric properties of the psychosomatic problems scale: A Rasch analysis of adolescent data. Social Indicators Research, 86(3), 511–523. https://doi.org/10.1007/s11205-007-9186-3

Helda, T., Atmazaki, & Gani, E. (2024). Validity and practicality of discovery-group learning models to improve 21st-century skills of high school students. REFLections, 31(3), 1408–1433. https://doi.org/10.61508/refl.v31i3.277735

Herawati, N. S., & Muhtadi, A. (2018). Pengembangan modul elektronik interaktif pada mata pelajaran kimia kelas XI SMA. Jurnal Inovasi Teknologi Pendidikan, 5(2). https://doi.org/10.21831/jitp.v5i2.15424

Hermansson, L., Fisher, A., Bernspång, B., & Eliasson, A.-C. (2004). Assessment of capacity for myoelectric control: A new Rasch-built measure of prosthetic hand control. Journal of Rehabilitation Medicine, 1(1), 1–1. https://doi.org/10.1080/16501970410024280

Huda, A., Azhar, N., Almasri, A., Hartanto, S., & Anshari, K. (2020). Practicality and effectiveness test of graphic design learning media based on Android. International Journal of Interactive Mobile Technologies, 14(4), 192. https://doi.org/10.3991/ijim.v14i04.12737

Hudha, M. N., Gunawan, K. D. H., Ramawati, D. S. K., & Nisa’, S. K. (2025). Exploring science education students’ understanding of nuclear physics concepts through field study implementation with non-stationary calorimetry methods. Momentum: Physics Education Journal, 9(2), 366–373. https://doi.org/10.21067/mpej.v9i2.12855

Hughes, S. E., Haroon, S., Subramanian, A., McMullan, C., Aiyegbusi, O. L., … & Calvert, M. J. (2022). Development and validation of the symptom burden questionnaire for long COVID (SBQ-LC): Rasch analysis. BMJ, 377, e070230. https://doi.org/10.1136/bmj-2022-070230

Istiyono, E., Dwandaru, W. S. B., Fenditasari, K., Ayub, M. R. S. S. N., & Saepuzaman, D. (2023). Development of a four-tier diagnostic test based on modern test theory in physics education. European Journal of Educational Research, 12(1), 371–385. https://doi.org/10.12973/eu-jer.12.1.371

Jaya, A. A. N. A., Prabandari, L. P. C., Ridhani, J., & Gunawan, K. D. H. (2025). Designing human-centered virtual reality to improve student earthquake preparedness in Bali. Journal of Environment and Sustainability Education, 3(4), 552–560. https://doi.org/10.62672/joease.v3i4.136

Jufriadi, A., Huda, C., Aji, S. D., Pratiwi, H. Y., & Ayu, H. D. (2022). Analisis keterampilan abad 21 melalui implementasi Kurikulum Merdeka Belajar Kampus Merdeka. Jurnal Pendidikan dan Kebudayaan, 7(1), 39–53. https://doi.org/10.24832/jpnk.v7i1.2482

Keller, L., Michelsen, G., Dür, M., Bachri, S., & Zint, M. (2023). Digitalization, new media, and education for sustainable development. IGI Global. https://doi.org/10.4018/978-1-7998-5033-5

Kim, Y., & Thayne, J. (2015). Effects of learner–instructor relationship-building strategies in online video instruction. Distance Education, 36(1), 100–114. https://doi.org/10.1080/01587919.2015.1019965

Kompar, F. (2018). “Mile deep” digital tools. Teacher Librarian, 45(3), 28–31.

Kumar, R., & Pande, N. (2017). Technology-mediated learning paradigm and the blended learning ecosystem: What works for working professionals? Procedia Computer Science, 122, 167–175. https://doi.org/10.1016/j.procs.2017.11.481

Lee, S. A., Lee, M., & Jeong, M. (2021). The role of virtual reality on information sharing and seeking behaviors. Journal of Hospitality and Tourism Management, 46, 222–231. https://doi.org/10.1016/j.jhtm.2020.12.010

Leopold, A. T., Ratcheva, V., & Zahidi, S. (2018). The future of jobs report 2018. World Economic Forum. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf

Lestari, D. P., Paidi, & Suwarjo. (2024). Development and validation of the inquiry-based nature of science and argumentation model. International Journal of Education and Practice, 12(2), 189–206. https://doi.org/10.18488/61.v12i2.3657

Maharcika, A. A. M., Suarni, N. K., & Gunamantha, I. M. (2021). Pengembangan e-modul berbasis Flipbook Maker. PENDASI: Jurnal Pendidikan Dasar Indonesia, 5(2). https://doi.org/10.23887/jurnal_pendas.v5i2.240

Mak, E. B., Conrad, K., Stuck, R., & Matters, M. (2006). Theoretical model and Rasch analysis to develop a revised foot function index. Foot & Ankle International, 27(7), 519–527. https://doi.org/10.1177/107110070602700707

Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the effect of pre-training when learning through immersive virtual reality and video. Computers & Education, 140, 103603. https://doi.org/10.1016/j.compedu.2019.103603

Mills, K. A., & Brown, A. (2022). Immersive virtual reality for digital media making: Transmediation is key. Learning, Media and Technology, 47(2), 212–225.

Pallant, J. F., & Tennant, A. (2007). An introduction to the Rasch measurement model: An example using HADS. British Journal of Clinical Psychology, 46(1), 1–18. https://doi.org/10.1348/014466506X96931

Prasasti, R. D., & Anas, N. (2023). Pengembangan media digital berbasis flipbook untuk meningkatkan kemampuan berpikir kritis pada peserta didik. Munaddhomah: Jurnal Manajemen Pendidikan Islam, 4(3), 694–705. https://doi.org/10.31538/munaddhomah.v4i3.589

Rahmawati, S., Setiyowati, A. J., & Eva, N. (2023). A guidebook of group guidance services with role play contains Welas Asih to prevent verbal bullying. Munaddhomah: Jurnal Manajemen Pendidikan Islam, 4(4), 825–833. https://doi.org/10.31538/munaddhomah.v4i4.660

Richard, J. (2012, April 27). SAMR and the integration of tech standards [Blog post]. https://jrichard64.wordpress.com/2012/04/27/samr-and-the-integration-of-tech-standards

Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271–282. https://doi.org/10.1177/014662169001400305

Sardi, J. (2024). Practicality of mobile-based learning with project-based learning approach in electric motor installation. International Journal of Information and Education Technology, 14(8), 1127–1135. https://doi.org/10.18178/ijiet.2024.14.8.2141

Sari, N., Murniati, & Ilyas, S. (2020). The implementation of problem-based learning modules to reduce misconceptions on Newton’s law topic. Journal of Physics: Conference Series, 1460(1), 012137. https://doi.org/10.1088/1742-6596/1460/1/012137

Scaife, T. M., & Heckler, A. F. (2010). Student understanding of the direction of the magnetic force on a charged particle. American Journal of Physics, 78(8), 869–876. https://doi.org/10.1119/1.3386587

Shanab, H., Mehta, A., & Bufasi, E. (2025). Misconceptions in fundamental physics among medical students. Physics Education, 60(6), 065017. https://doi.org/10.1088/1361-6552/ae0857

Smith, A. B., Rush, R., Fallowfield, L. J., Velikova, G., & Sharpe, M. (2008). Rasch fit statistics and sample size considerations for polytomous data. BMC Medical Research Methodology, 8, 33. https://doi.org/10.1186/1471-2288-8-33

Smith, R. M. (2003). Rasch measurement models: Interpreting WINSTEPS and FACETS output. JAM Press.

Soeharto, S., & Csapó, B. (2022). Assessing Indonesian student inductive reasoning: Rasch analysis. Thinking Skills and Creativity, 46, 101132. https://doi.org/10.1016/j.tsc.2022.101132

Tarigan, E. F., Nilmarito, S., Islamiyah, K., Darmana, A., & Suyanti, R. D. (2022). Analisis instrumen tes menggunakan Rasch model dan SPSS 22.0. Jurnal Inovasi Pendidikan Kimia, 16(2), 305–330. https://doi.org/10.15294/jipk.v16i2.30530

Tennant, A., & Conaghan, P. G. (2007). The Rasch measurement model in rheumatology. Arthritis & Rheumatism, 57(8), 1358–1362. https://doi.org/10.1002/art.23108

Tesio, L., Caronni, A., Simone, A., Kumbhare, D., & Scarano, S. (2024). Interpreting results from Rasch analysis 2: Advanced model applications and data-model fit assessment. Disability and Rehabilitation, 46(3). https://doi.org/10.1080/09638288.2023.2169772

Wang, W.-C., & Chen, C.-T. (2005). Item parameter recovery and fit statistics of the WINSTEPS program for Rasch models. Educational and Psychological Measurement, 65(3), 376–404. https://doi.org/10.1177/0013164404268673

Wati, M., Sutiniasih, N., Misbah, Mahtari, S., Annur, S., & Mastuang. (2020). Developing physics teaching materials based on authentic learning to train problem-solving skills. Journal of Physics: Conference Series, 1567(3), 032084. https://doi.org/10.1088/1742-6596/1567/3/032084

Wongpakaran, N., Wongpakaran, T., Pinyopornpanish, M., Simcharoen, S., Suradom, C., Varnado, P., & Kuntawong, P. (2020). Development and validation of a 6-item Revised UCLA Loneliness Scale (RULS-6) using Rasch analysis. British Journal of Health Psychology, 25(2), 233–256. https://doi.org/10.1111/bjhp.12404

Published

2025-12-20

How to Cite

Validity Analysis of VR-Based Particle Dynamics Module Development Using the Rasch Model. (2025). Online Learning In Educational Research (OLER), 5(2), 441-455. https://doi.org/10.58524/oler.v5i2.803