Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education

Authors

  • Ajub Ajulian Zahra Macrina Diponegoro University, Indonesia
  • Iwan Setiawan Diponegoro University, Indonesia
  • Darjat Diponegoro University, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i4.1696

Keywords:

Technology Acceptance Model, Perceived Immersive Learning, Virtual Reality, Behavioral Intention, Higher Education

Abstract

This study examines the adoption of Virtual Reality (VR)-based learning in Indonesian higher education by extending the Technology Acceptance Model (TAM) through Perceived Immersive Learning (PIL). The study investigates how perceived usefulness, perceived ease of use, PIL, behavioral intention, and self-reported usage behavior are related. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to data collected from 180 university students with experience using VR-based immersive learning technologies. The measurement and structural models were analyzed using SmartPLS 4. Results show that perceived ease of use significantly influences perceived usefulness and PIL, while perceived usefulness also has a significant positive effect on PIL. In addition, PIL significantly affects behavioral intention, which subsequently influences self-reported VR usage behavior. The model explains 58.6% of the variance in behavioral intention (R² = 0.586), indicating substantial explanatory power. These findings demonstrate that immersive learning experiences play a central role in translating technology perceptions into behavioral intention and subsequent usage behavior. The study contributes to TAM literature by empirically positioning PIL as a mediating construct and offers practical insights for universities seeking to design more engaging, usable, and meaningful VR-based learning environments that can strengthen students' acceptance and sustained engagement with VR.

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References

[1] S. Pitsikalis, I. Lasica, and A. Kostas, “Educational Design Guidelines for Teaching with Immersive Technologies — Updating Learning Outcomes of the European Qualification Framework,” Trends High. Educ., pp. 1091–1108, 2024, doi: 10.3390/higheredu3040064.

[2] A. Cabrera-duffaut, A. M. Pinto-llorente, A. Iglesias-rodríguez, and A. Cabrera-duffaut, “Immersive learning platforms : analyzing virtual reality contribution to competence development in higher education — a systematic literature review,” Front. Educ., no. July, 2024, doi: 10.3389/feduc.2024.1391560.

[3] M. A. Kuhail, A. Elsayary, S. Farooq, and A. Alghamdi, “Exploring Immersive Learning Experiences : A Survey,” Informatics, 2022, doi: 10.3390/informatics9040075.

[4] M. Won et al., “Computers & Education Diverse approaches to learning with immersive Virtual Reality identified from a systematic review,” Comput. Educ., vol. 195, no. December 2022, p. 104701, 2023, doi: 10.1016/j.compedu.2022.104701.

[5] H. Khamis, A. H. Jantan, N. A. Roslan, and L. N. Abdullah, “Immersive Learning Factors Using Virtual Reality In Higher Education: A Systematic Literature Review (SLR),” Environ. Proc. J., vol. 10, no. SI32, pp. 53–62, 2025, doi: 10.21834/e-bpj.v10iSI32.7053.

[6] R. Susilana, L. Dewi, and G. Rullyana, “Exploring Student Perceptions of Virtual Learning Experiences: A Study of the Use of the Spatial.io 3D Metaverse Platform,” J. Educ. Technol., vol. 8, no. 4, pp. 673–683, 2024, doi: 10.23887/jet.v8i4.85190.

[7] Q. Tang, Y. Wang, H. Liu, and Q. Liu, “Experiencing an art education program through immersive virtual reality or iPad : Examining the mediating e ects of sense of presence and extraneous cognitive load on enjoyment , attention , and retention,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.957037.

[8] G. B. Petersen, G. Petkakis, and G. Makransky, “A study of how immersion and interactivity drive VR learning,” Comput. Educ., vol. 179, pp. 1–16, 2022, doi: 10.1016/j.compedu.2021.104429.

[9] G. Makransky and G. B. Petersen, “The Cognitive Affective Model of Immersive Learning ( CAMIL ): a Theoretical Research-Based Model of Learning in Immersive Virtual Reality Content courtesy of Springer Nature , terms of use apply . Rights reserved .,” Educ. Psychol. Rev., 2021, doi: 10.1007/s10648-020-09586-2.

[10] M. Wilkinson, S. Brantley, and J. Feng, “A Mini Review of Presence and Immersion in Virtual Reality,” Proc. Hum. Factors Ergon. Soc., vol. 65, no. 1, pp. 1099–1103, 2021, doi: 10.1177/1071181321651148.

[11] C. R. Guerra-Tamez, “The Impact of Immersion through Virtual Reality in the Learning Experiences of Art and Design Students: The Mediating Effect of the Flow Experience,” Educ. Sci., vol. 13, pp. 1–18, 2023, doi: 10.3390/educsci13020185.

[12] X. Huang, Q. Zhao, and M. Shawler, “Learning in an Immersive VR Environment : Role of Learner Characteristics and Relations Between Learning and Psychological Outcomes,” J. Educ. Technol. Syst., vol. 53, no. 1, 2024, doi: 10.1177/00472395231216943.

[13] D. Hamilton, J. McKechnie, E. Edgerton, and C. Wilson, “Immersive virtual reality as a pedagogical tool in education: a systematic literature review of quantitative learning outcomes and experimental design,” J. Comput. Educ., vol. 8, no. 1, pp. 1–32, 2021, doi: 10.1007/s40692-020-00169-2.

[14] N. Yu, W. Shi, W. Dong, and R. Kang, “The Impact of Virtual Reality Immersion on Learning Outcomes : A Comparative Study of Declarative and Procedural Knowledge Acquisition,” Behav. Sci. (Basel)., pp. 1–19, 2025, doi: 10.3390/bs15101322.

[15] X. Tang, A. Chik, and L. Tang, “Optimizing immersive learning in crisis management : cognitive and psychological mechanisms of VR training,” Front. Psychol., vol. 16, no. 1695101, pp. 1–11, 2025, doi: 10.3389/fpsyg.2025.1695101.

[16] B. De Witte et al., “Immersive virtual reality learning and cognitive load: A multiple-day field study,” Comput. Human Behav., vol. 176, pp. 1–16, 2025, doi: 10.1016/j.chb.2025.108853.

[17] J. A. M. García, C. G. Gomez, L. A. Tapia, and M. J. Schlosser, “Applying the technology acceptance model to online self-learning: A multigroup analysis,” J. Innov. Knowl., vol. 9, 2024, doi: 10.1016/j.jik.2024.100571.

[18] F. D. Davis, “User acceptance of information technology: system characteristics, user perceptions and behavioral impacts,” Man-machine Stud., vol. 38, no. 3, pp. 475–487, 1993, doi: 10.1006/imms.1993.1022.

[19] S. Sipone, V. Abella, M. Rojo, and J. L. Moura, “Sustainable mobility learning : Technological acceptance model for gamified experience with ClassCraft in primary,” Educ. Inf. Technol., vol. 28, no. 12, pp. 16177–16200, 2023, doi: 10.1007/s10639-023-11851-0.

[20] D. R. Jovanka et al., “Determinants of e-Learning Services : Indonesian Open University,” Cogent Educ., vol. 10, no. 1, 2023, doi: 10.1080/2331186X.2023.2183703.

[21] A. S. Al-Adwan, N. Li, A. Al-Adwan, G. A. Abbasi, N. A. Albelbisi, and A. Habibi, “"Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms",” Educ. Inf. Technol., vol. 28, no. 11, pp. 15381–15413, 2023, doi: 10.1007/s10639-023-11816-3.

[22] C.-Q. Chen, C.-Y. Wang, X.-F. Shan, L. Zhan, and S.-J. Chen, “An empirical investigation of reasons influencing pre-service teachers acceptance and rejection of immersive virtual reality usage,” Teach. Teach. Educ., vol. 137, pp. 1–11, 2024, doi: 10.1016/j.tate.2023.104391.

[23] F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008.

[24] J. Radianti, T. A. Majchrzak, J. Fromm, and I. Wohlgenannt, “A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda,” Comput. Educ., vol. 147, no. November 2019, p. 103778, 2020, doi: 10.1016/j.compedu.2019.103778.

[25] G. Makransky and L. Lilleholt, “A Structural Equation Modeling Investigation of the Emotional Value of Immersive Virtual Reality in Education,” Educ. Technol. Res. Dev., vol. 66, pp. 1–24, Feb. 2018, doi: 10.1007/s11423-018-9581-2.

[26] M. Fishbein and I. Ajzen, “Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research,” Addison-Wesley Publishing Company, 1975, pp. 510–520.

[27] D. A. Sprenger and A. Schwaninger, “Technology acceptance of four digital learning technologies (classroom response system, classroom chat, e-lectures, and mobile virtual reality) after three months’ usage,” Int. J. Educ. Technol. High. Educ., vol. 18, no. 1, 2021, doi: 10.1186/s41239-021-00243-4.

[28] L. Xue, J. Mahat, N. Ghazali, and X. Shi, “Technology Acceptance Model (TAM) in Artificial Intelligence in Higher Education (AIHEd): A Systematic Literature Review,” J. Institutional Res. South East Asia, vol. 23, no. 1, pp. 355–375, 2025, doi: 10.1177/21582440251409441.

[29] T. T. Back, A. M. Tinga, P. Nguyen, and M. M. Louwerse, “Benefits of immersive collaborative learning in CAVE-based virtual reality,” Int. J. Educ. Technol. High. Educ., vol. 17, no. 51, pp. 1–18, 2020, doi: 10.1186/s41239-020-00228-9.

[30] M. Slater and S. Wilbur, “A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments,” Presence Teleoperators virtual Environ., vol. 6, no. 6, pp. 603–616, 1997, doi: 10.1162/pres.1997.6.6.603.

[31] M. Slater, “Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments,” Philos. Trans. R. Soc. B Biol. Sci., vol. 364, no. 1535, pp. 3549–3557, Dec. 2009, doi: 10.1098/RSTB.2009.0138.

[32] J. Piaget, The Origins of Intelligence in Children, Fourth., vol. 9, no. 1. New York: International Universities Press Inc, 1952.

[33] L. S. Vygotsky, Mind in Society. Harvard University Press, 1978.

[34] D. A. Kolb, Experiential Learning: Experience as the Source of Learning and Development. New Jersey: Prentice Hall, 1984.

[35] K. H. Cheng and C. C. Tsai, “Students’ motivational beliefs and strategies, perceived immersion and attitudes towards science learning with immersive virtual reality: A partial least squares analysis,” Br. J. Educ. Technol., vol. 51, no. 6, pp. 2139–2158, 2020, doi: 10.1111/bjet.12956.

[36] S. Aufenanger et al., “Immersive virtual reality learning environments for higher education: A student acceptance study,” Comput. Educ. X Real., vol. 7, pp. 1–14, 2025, doi: 10.1016/j.cexr.2025.100105.

[37] R. A. Ali, M. Soliman, M. R. Weahama, M. Assalihee, and I. Mahmud, “Investigating continuous intention to use metaverse in higher education institutions: a dual-staged structural equation modeling-artificial neural network approach,” Smart Learn. Environ., vol. 12, no. 3, pp. 1–25, 2025, doi: 10.1186/s40561-024-00357-y.

[38] G. Kazakou, G. Koutromanos, and C. Christogiannis, “Examining teachers’ acceptance of augmented reality smart glasses through the immersive technologies acceptance model,” Educ. Inf. Technol., vol. 30, pp. 26329–26372, 2025, doi: 10.1007/s10639-025-13687-2.

[39] S. S. Man, Y. Fang, A. H. S. Chan, and J. Han, “VR technology acceptance for English learning amongst secondary school students: role of classroom climate and language learning anxiety,” Educ. Inf. Technol., vol. 30, pp. 4131–4155, 2024, doi: 10.1007/s10639-024-12969-5.

[40] G. M. Serna-Mendiburu and C. R. Guerra-Tamez, “Shaping the future of creative education : the transformative power of VR in art and design learning,” Front. Educ., vol. 9, pp. 1–16, 2024, doi: 10.3389/feduc.2024.1388483.

[41] T. A. Mikropoulos and A. Natsis, “Educational virtual environments: A ten-year review of empirical research (1999-2009),” Comput. Educ., vol. 56, no. 3, pp. 769–780, 2011, doi: 10.1016/j.compedu.2010.10.020.

[42] A. F. Di Natale, S. Bartolotta, A. Gaggioli, G. Riva, and D. Villani, “Exploring students’ acceptance and continuance intention in using immersive virtual reality and metaverse integrated learning environments: The case of an Italian university course,” Educ. Inf. Technol., vol. 29, pp. 14749–14768, 2024, doi: 10.1007/s10639-023-12436-7.

[43] T. M. Pangestuti, M. Kamil, and S. Sardin, “Model Pelatihan Andragogi Berbasis Media Virtual Reality Dalam Peningkatan Kesiapsiagaan Bencana,” Indones. J. Adult Community Educ., vol. 2, no. 1, pp. 38–43, 2020, doi: 10.17509/ijace.v2i1.28289.

[44] Husamah, D. K. Ardiyani, P. H. Wijayati, and D. W. Ekowati, Metode Pendidikan Orang Dewasa : Pendekatan Andragogi dan Transformasi, Edisi Indo. Papua Selatan: PT. Akselerasi Karya Mandiri, 2025.

[45] S. Wardat and M. Akour, “Investigating the Adoption of Metaverse-Based Immersive Learning in TESOL,” Emerg. Sci. J., vol. 8, pp. 396–409, 2024, doi: 10.28991/ESJ-2024-SIED1-023.

[46] Q. Wang, H. Zhan, Y. Liang, P. Li, and L. Ai, “Perceived ease of use , perceived enjoyment , and continuance intention as predictors of medical students ’ well-being in hybrid learning : an extended TAM approach,” Front. Psychol., vol. 16, pp. 1–10, 2025, doi: 10.3389/fpsyg.2025.1704678.

[47] L. Zhang, Y. Yin, L. Liu, and S. Yang, “Exploring the effects of AR presence and personal norms on museum visitor pro-environmental behaviour intentions,” Sci. Rep., vol. 15, no. 25633, pp. 1–13, Jul. 2025, doi: 10.1038/s41598-025-11119-2.

[48] E. Y. cheung Wong, R. T. yin Hui, and H. Kong, “Perceived usefulness of, engagement with, and effectiveness of virtual reality environments in learning industrial operations: the moderating role of openness to experience,” Virtual Real., vol. 27, no. 3, pp. 2149–2165, 2023, doi: 10.1007/s10055-023-00793-0.

[49] S. Solmaz, K. Gerling, L. Kester, and T. Van Gerven, “Behavioral intention, perception and user assessment in an immersive virtual reality environment with CFD simulations,” Virtual Real., vol. 28, no. 2, pp. 1–20, 2024, doi: 10.1007/s10055-024-00985-2.

[50] A. Şimşek, Salih, G. Ş. Tepetaş, and C. Mazhar, “Extending the TAM framework : Exploring learning motivation and agility in educational adoption of generative AI,” Educ. Inf. Technol., vol. 30, pp. 20913–20942, 2025, doi: 10.1007/s10639-025-13591-9.

[51] J. W. Creswell and J. D. Creswell, Research Design Qualitative, Quantitative, and Mixed Methods Approaches. London: SAGE Publications, 2018.

[52] J. Hair, G. T. M. Hult, C. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed. Australia: SAGE Publication, 2022.

[53] P. Limpinan, T. Jantakoon, and S. Trisupakitti, “Technology Acceptance Model of Immersive Microlearning in STEAM Education: Insights from a PLS-SEM Analysis,” High. Educ. Stud., vol. 15, no. 4, pp. 85–100, 2025, doi: 10.5539/hes.v15n4p85.

[54] F. Wu and G. Xie, “Research on the influence of perceived quality on users’ continuance usage intention of online live streaming class platforms: the mediating role of flow experience and the moderating impact of perceived usefulness,” Front. Psychol., vol. 15, pp. 1–13, 2024, doi: 10.3389/fpsyg.2024.1455597.

[55] J. C. Tu and X. H. Jia, “A Study on Immersion and Intention to Pay in AR Broadcasting: Validating and Expanding the Hedonic Motivation System Adoption Mode,” Sustain., vol. 16, no. 5, 2024, doi: 10.3390/su16052040.

[56] R. Firdaus et al., “Adaptation and Validation of the Interprofessional Socialization and Valuing Scale (ISVS-24) for Utilization in Multiplayer Virtual Reality Environments,” F1000Research, vol. 14, no. 169, pp. 1–34, 2026, doi: 10.12688/f1000research.160939.3.

[57] L. Bareišytė et al., “Questionnaires for evaluating virtual reality: A systematic scoping review,” Comput. Hum. Behav. Reports, vol. 16, 2024, doi: 10.1016/j.chbr.2024.100505.

[58] N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” Int. J. e-Collaboration, vol. 11, no. 4, pp. 1–10, 2015, doi: 10.4018/ijec.2015100101.

[59] G. Shmueli et al., “Predictive model assessment in PLS-SEM: guidelines for using PLSpredict,” Eur. J. Mark., vol. 53, no. 11, pp. 2322–2347, 2019, doi: 10.1108/EJM-02-2019-0189.

[60] J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, 2015, doi: 10.1007/s11747-014-0403-8.

[61] I. Ajzen, “The Theory of Planned Behavior,” Organ. Behav. Hum. Decis. Process., vol. 50, no. 2, pp. 179–211, 1991, doi: 10.1016/0749-5978(91)90020-T.

[62] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q. Manag. Inf. Syst., vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540.

[63] X. Zhang, Y. Huang, M. Chen, and F. Wang, “Exploring the Factors Affecting College Students’ Intention to Use Online Teaching Platforms Through an Extended Technology Acceptance Model,” Int. J. Web-Based Learn. Teach. Technol., vol. 19, no. 1, pp. 1–16, 2024, doi: 10.4018/IJWLTT.348656.

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2026-08-22

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