Technology Readiness and Learner Engagement in Adaptive Immersive Microlearning: A Mediation Analysis

R. Parhana; K. Santhanalakshmi; Martin Selvakumar Mohanan; B. Sambath Kumar; V. K. Ajay; V. Sathya

doi:10.57159/jcmm.5.4.261000

Authors

R. Parhana Faculty of Management, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India https://orcid.org/0009-0001-0757-1901
K. Santhanalakshmi Faculty of Management, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-6980-2587
Martin Selvakumar Mohanan Great Lakes Institute of Management, Chennai, Tamil Nadu, India https://orcid.org/0000-0003-4412-9335
B. Sambath Kumar Department of Management Studies, T. J. S. Engineering College, Thiruvallur, Tamil Nadu, India https://orcid.org/0009-0002-7979-1433
V. K. Ajay Department of Data Analytics, Saveetha College of Liberal Arts and Sciences (SIMATS), Chennai, Tamil Nadu, India https://orcid.org/0000-0002-9265-4727
V. Sathya Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India https://orcid.org/0000-0002-0355-1401

DOI:

https://doi.org/10.57159/jcmm.5.4.261000

Keywords:

Technology Readiness, Immersive Learning, Adaptive Learning Systems, Microlearning, Learner Engagement, Cognitive Load

Abstract

This study examines how technology readiness relates to learner engagement, knowledge acquisition, and perceived usability in adaptive immersive microlearning. Drawing on Technology Readiness Theory and Cognitive Load Theory, it investigates whether the four readiness dimensions, namely optimism, innovativeness, discomfort, and insecurity, are directly associated with learning outcomes and whether engagement operates as a mediating mechanism. A total of 200 participants from higher education institutions and professional training programs completed a predictive cross-sectional study. Data were collected using the Technology Readiness Index 2.0, a WebXR-based adaptive troubleshooting module, a six-item prior-knowledge pretest, a twelve-item engagement scale, a ten-item knowledge posttest, and the System Usability Scale. Optimism and innovativeness were positively associated with engagement, knowledge acquisition, and perceived usability, whereas discomfort and insecurity were negatively associated with these outcomes. Mediation analysis indicated partial indirect effects through engagement, with significant indirect paths across all four dimensions when each was modeled separately. In multivariate models, optimism, innovativeness, and discomfort retained independent effects, whereas the contribution of insecurity overlapped substantially with that of discomfort. Structural equation modeling explained 35% of the variance in engagement, 27% in knowledge acquisition, and 36% in perceived usability. Psychological readiness toward technology is therefore a meaningful predictor of success in adaptive immersive learning rather than a background characteristic. The paper also advances design propositions for readiness-aware adaptive systems built with WebXR, offered as directions for future research rather than validated prescriptions.

Author Biographies

R. Parhana, Faculty of Management, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India

Faculty of Management

V. Sathya, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

Department of CSE

References

[1] M. Jainuri, Kamid, Syaiful, and N. Huda, "Microlearning effectiveness in higher education: A systematic review and meta-analysis of student retention and learning outcomes," MATHEMA: Jurnal Pendidikan Matematika, vol. 7, no. 2, pp. 630-642, 2025.

[2] A. Cabrera-Duffaut, A. M. Pinto-Llorente, and A. Iglesias-Rodríguez, "Immersive learning platforms: analyzing virtual reality contribution to competence development in higher education, a systematic literature review," Frontiers in Education, vol. 9, p. 1391560, 2024.

[3] R. Villena-Taranilla, S. Tirado-Olivares, R. Cózar-Gutiérrez, and J. A. González-Calero, "Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis," Educational Research Review, vol. 35, p. 100434, 2022.

[4] I. Onopriienko, K. Onopriienko, and S. Bourekkadi, "Immersive technologies in adult learning as an innovative marketing tool in the educational market," Business Ethics and Leadership, vol. 7, no. 2, pp. 63-72, 2023.

[5] E. Al Khalifah, R. Hammady, M. Abdelrahman, O. Al-Shamaileh, M. Marghany, H. El-Jarn, A. Darwish, and Y. Kurt, "Technology anxiety in virtual reality adoption: examining the impact of age, past experience, and cybersickness," IEEE Access, vol. 13, pp. 71858-71879, 2025.

[6] F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, "User acceptance of computer technology: a comparison of two theoretical models," Management Science, vol. 35, no. 8, pp. 982-1003, 1989.

[7] J. H. Han and H. J. Sa, "Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): the COVID-19 situation in Korea," Asia Pacific Education Review, vol. 23, no. 3, pp. 403-415, 2022.

[8] P. J. Pires, B. A. da Costa Filho, and R. Mendes Junior, "Revisiting the technology readiness index (TRI 2.0): a study applied to students accessing digital social networks in Brazil," RELCASI, vol. 16, no. 1, p. 2, 2024.

[9] A. Parasuraman and C. L. Colby, "An updated and streamlined technology readiness index: TRI 2.0," Journal of Service Research, vol. 18, no. 1, pp. 59-74, 2015.

[10] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "User acceptance of information technology: toward a unified view," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003.

[11] A. Skulmowski and K. M. Xu, "Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load," Educational Psychology Review, vol. 34, no. 1, pp. 171-196, 2022.

[12] E. Serrano-Ausejo and E. Mårell-Olsson, "Opportunities and challenges of using immersive technologies to support students' spatial ability and 21st-century skills in K-12 education," Education and Information Technologies, vol. 29, no. 5, pp. 5571-5597, 2024.

[13] L. J. Hsin, Y. P. Chao, H. H. Chuang, T. B. J. Kuo, C. C. H. Yang, C. G. Huang, C. J. Kang, W. N. Lin, T. J. Fang, H. Y. Li, and L. A. Lee, "Mild simulator sickness can alter heart rate variability, mental workload, and learning outcomes in a 360-degree virtual reality application for medical education," Virtual Reality, vol. 27, no. 4, pp. 3345-3361, 2023.

[14] J. Zhao, X. Li, and Z. Gao, "From innovativeness to insecurity: unveiling the facets of translation technology use behavior among EFL learners using TRI 2.0," Humanities and Social Sciences Communications, vol. 12, no. 1, p. 436, 2025.

[15] A. S. Al-Adwan, A. Al-Adwan, N. Li, M. A. Fauzi, R. M. S. Jafar, A. Habibi, and M. Falahat, "Immersive learning meets theory: modeling Eduverse adoption in higher education," Journal of Information Technology Education: Research, vol. 24, p. 42, 2025.

[16] J. Buchner, K. Buntins, and M. Kerres, "The impact of augmented reality on cognitive load and performance: A systematic review," Journal of Computer Assisted Learning, vol. 38, no. 1, pp. 285-303, 2022.

[17] G. Makransky and L. Lilleholt, "A structural equation modeling investigation of the emotional value of immersive virtual reality in education," Educational Technology Research and Development, vol. 66, no. 5, pp. 1141-1164, 2018.

[18] M. W. Bhatt, M. R. M. Veeramanickam, J. R. Ruffner, C. Navarro, M. S. Mia, G. Kaur, and M. Soni, "Enhancing engineering student engagement and learning outcomes through WebVR and wearable sensor integration with immersive learning," Discover Sustainability, vol. 6, no. 1, p. 590, 2025.

[19] M. Blut and C. Wang, "Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage," Journal of the Academy of Marketing Science, vol. 48, no. 4, pp. 649-669, 2020.

[20] A. Parasuraman, "Technology readiness index (TRI): a multiple-item scale to measure readiness to embrace new technologies," Journal of Service Research, vol. 2, no. 4, pp. 307-320, 2000.

[21] 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," Educational Psychology Review, vol. 33, no. 3, pp. 937-958, 2021.

[22] F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989.

[23] P. Vlachogianni and N. Tselios, "Perceived usability evaluation of educational technology using the system usability scale (SUS): A systematic review," Journal of Research on Technology in Education, vol. 54, no. 3, pp. 392-409, 2022.

[24] R. Walczuch, J. Lemmink, and S. Streukens, "The effect of service employees' technology readiness on technology acceptance," Information and Management, vol. 44, no. 2, pp. 206-215, 2007.

[25] J. Sweller, "Cognitive load theory," in Psychology of Learning and Motivation (B. H. Ross, ed.), vol. 55, pp. 37-76, San Diego, CA, USA: Academic Press, 2011.

[26] J. L. Plass, R. Moreno, and R. Brünken, eds., Cognitive Load Theory. Cambridge, U.K.: Cambridge University Press, 2010.

[27] J. Sweller, J. J. G. van Merriënboer, and F. Paas, "Cognitive architecture and instructional design: 20 years later," Educational Psychology Review, vol. 31, no. 2, pp. 261-292, 2019.

[28] D. Wang and X. Huang, "Transforming education through artificial intelligence and immersive technologies: enhancing learning experiences," Interactive Learning Environments, vol. 33, no. 7, pp. 4546-4565, 2025.

[29] T. H. Sam, H. Mu'min, M. Riyanto, A. Iskandar, S. Abid, and M. Melati, "Immersive technologies and academic performance in online learning: A sequential mediation analysis," Social Sciences and Humanities Open, vol. 12, p. 102192, 2025.

[30] N. Wenk, J. Penalver-Andres, K. A. Buetler, T. Nef, R. M. Müri, and L. Marchal-Crespo, "Effect of immersive visualization technologies on cognitive load, motivation, usability, and embodiment," Virtual Reality, vol. 27, no. 1, pp. 307-331, 2023.

[31] M. Kadri, F. E. Boubakri, G. J. Hwang, F. Z. Kaghat, A. Azough, and K. Alaoui Zidani, "C-IVAL: a longitudinal study of knowledge retention and technology acceptance in collaborative virtual reality-based medical education," IEEE Access, vol. 13, pp. 16055-16071, 2025.

[32] Y. Samuel, M. Brennan-Tonetta, J. Samuel, R. Kashyap, V. Kumar, S. K. Kaashyap, N. Chidipothu, I. Anand, and P. Jain, "Cultivation of human centered artificial intelligence: culturally adaptive thinking in education (CATE) for AI," Frontiers in Artificial Intelligence, vol. 6, p. 1198180, 2023.

[33] J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, "School engagement: potential of the concept, state of the evidence," Review of Educational Research, vol. 74, no. 1, pp. 59-109, 2004.

[34] M. T. H. Chi and R. Wylie, "The ICAP framework: linking cognitive engagement to active learning outcomes," Educational Psychologist, vol. 49, no. 4, pp. 219-243, 2014.

[35] M. S. Setia, "Methodology series module 3: Cross-sectional studies," Indian Journal of Dermatology, vol. 61, no. 3, pp. 261-264, 2016.

[36] J. Maksimović and J. Evtimov, "Positivism and post-positivism as the basis of quantitative research in pedagogy," Research in Pedagogy, vol. 13, no. 1, pp. 208-218, 2023.

[37] R. J. O. Carvalho, Adopting immersive web environments in education: educators' perspectives and practical implications. PhD thesis, Universidade de Aveiro, Aveiro, Portugal, 2025.

[38] J. W. Long, B. Masters, P. Sajjadi, C. Simons, and T. D. Masterson, "The development of an immersive mixed-reality application to improve the ecological validity of eating and sensory behavior research," Frontiers in Nutrition, vol. 10, p. 1170311, 2023.

[39] D. Saputra, E. A. Syah, and F. Darnis, "Usability testing on the Simponik website using the System Usability Scale (SUS)," Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 7, no. 4, pp. 2584-2592, 2022.

[40] J. Brooke, "SUS: A quick and dirty usability scale," in Usability Evaluation in Industry (P. W. Jordan, B. Thomas, B. A. Weerdmeester, and I. L. McClelland, eds.), pp. 189-194, London, U.K.: Taylor and Francis, 1996.

[41] B. M. Byrne, Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. New York, NY, USA: Routledge, 3 ed., 2016.

[42] A. F. Hayes, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY, USA: Guilford Press, 3 ed., 2022.

[43] P. M. Podsakoff, S. B. MacKenzie, J. Y. Lee, and N. P. Podsakoff, "Common method biases in behavioral research: a critical review of the literature and recommended remedies," Journal of Applied Psychology, vol. 88, no. 5, pp. 879-903, 2003.

[44] T. D. Little, W. A. Cunningham, G. Shahar, and K. F. Widaman, "To parcel or not to parcel: exploring the question, weighing the merits," Structural Equation Modeling, vol. 9, no. 2, pp. 151-173, 2002.

[45] B. G. Tabachnick and L. S. Fidell, Using Multivariate Statistics. Boston, MA, USA: Pearson, 7 ed., 2019.

[46] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis. Andover, U.K.: Cengage Learning, 8 ed., 2019.

[47] L. T. Hu and P. M. Bentler, "Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives," Structural Equation Modeling, vol. 6, no. 1, pp. 1-55, 1999.