Modelling user acceptance of personalised learning apps in high schools using the SEM approach
DOI:
https://doi.org/10.59672/ijed.v6i3.4807Keywords:
Higher Education, Mobile Learning, Structural Equation Modeling, Technology Acceptance Model 3.Abstract
This research addresses the urgent need to understand user acceptance of personalised mobile learning applications in higher education, especially as digital learning becomes increasingly essential in post-pandemic education. The study employs a quantitative research design, utilising the Technology Acceptance Model 3 (TAM3) as the theoretical framework and Structural Equation Modelling (SEM) for analysis. The population comprises undergraduate students from various departments at STIKOM Uyelindo Kupang, selected using stratified random sampling to ensure representation across faculties. Data was collected through a validated questionnaire based on TAM3 constructs, and the instrument's validity and reliability were confirmed using Cronbach's Alpha, Composite Reliability (CR), and Average Variance Extracted (AVE). The results show that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) significantly influence Behavioural Intention (BI), while Social Influence (SI) and Facilitating Conditions (FC) also play important roles. Perceived Enjoyment (PE) enhances engagement, and Computer Anxiety negatively affects ease of use. The study concludes that TAM3 effectively models user acceptance in this context. Recommendations include improving app usability, providing institutional support, and designing engaging learning experiences to enhance the adoption and continued use of mobile learning technologies.
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References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 144, 103705.
Alyoussef, I. Y. (2021). Factors influencing students’ acceptance of m-learning in higher education: An application and extension of the UTAUT model. Electronics (Switzerland), 10(24). https://doi.org/10.3390/electronics10243171
Ambele, R. M., Kaijage, S. F., Dida, M. A., Trojer, L., & M. Kyando, N. (2022). A review of the Development Trend of Personalized learning Technologies and its Applications. International Journal of Advances in Scientific Research and Engineering, 08(11), 75–91. https://doi.org/10.31695/ijasre.2022.8.11.9
Ameen, N., Willis, R., & Shah, M. H. (2019). An examination of the gender gap in smartphone adoption and use in Arab countries: A cross-country comparison. Information Systems Frontiers, 21(3), 455–469.
ARAIN, A. A., HUSSAIN, Z., VIGHIO, M. S., & RIZVI, W. H. (2018). Factors Influencing Acceptance of Mobile Learning by Higher Education Students in Pakistan. Sindh University Research Journal -Science Series, 50(001), 141–146. https://doi.org/10.26692/surj/2018.01.0025
Blyznyuk, T., Budnyk, O., & Kachak, T. (2021). Boom in Distance Learning During the Coronavirus Pandemic: Challenges and Possibilities. Journal of Vasyl Stefanyk Precarpathian National University, 8(1), 90–98. https://doi.org/10.15330/jpnu.8.1.90-98
Citrawan, I. W., Widana, I. W., Sumandya, I. W., Widana, I. N. S., Mukminin, A., Arief, H., Razak, R. A., Hadiana, D., & Meter, W. (2024). Special education teachers’ ability in literacy and numeracy assessments based on local wisdom. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 8(1), 145-157. https://doi.org/10.22437/jiituj.v8i1.32608
Crompton, H., Burke, D., Gregory, K., & Gräbe, C. (2021). The use of mobile learning in science: A systematic review. Journal of Science Education and Technology, 30(5), 648–663.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology.
Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020). The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies. Educational Psychology Review, 32(2), 481–509. https://doi.org/10.1007/s10648-019-09510-3
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2020). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 22(4), 953–975.
Gusti, I., Yoga, P., Karisma, A., & Gui, A. (2023). Understanding E-Learning System Acceptance: an Empirical Analysis of Key Factors Among Elementary School Students Using TAM Model. Technology Acceptance Model) Jurnal TAM, 14(2), 213–220. Retrieved from https://jurnal.ftikomibn.ac.id/index.php/JurnalTam/index
Hamid, S. A., Muhisn, Z. A. A., Noori, A. S., & ... (2022). Mobile Learning in Higher Education through the COVID-19 Epidemic. Recent Trends in …, 5(June), 1–5. https://doi.org/10.5281/zenodo.6476912
Ifinedo, P. (2018). Determinants of students’ continuance intention to use blogs to enhance learning: An empirical investigation. Behaviour & Information Technology, 37(4), 376–389.
Ketchen, D. J. (2013). A Primer on Partial Least Squares Structural Equation Modeling. Long Range Planning, 46(1–2), 184–185. https://doi.org/10.1016/j.lrp.2013.01.002
Kim, S. H., & Lee, K. Y. (2020). Exploring determinants of mobile learning adoption in higher education. Interactive Learning Environments, 28(3), 347–362.
Maisha, K., & Shetu, S. N. (2023). Influencing factors of e-learning adoption amongst students in a developing country: the post-pandemic scenario in Bangladesh. Future Business Journal, 9(1), 1–16. https://doi.org/10.1186/s43093-023-00214-3
Masrek, M. N., & Samadi, I. (2017). Determinants of mobile learning adoption in higher education setting. Asian Journal of Scientific Research, 10(2), 60–69. https://doi.org/10.3923/ajsr.2017.60.69
Motia, M., & Maruf, T. I. (2024). Perceived Usefulness and Perceived Ease of Use in the Worth of Online Education System in Bangladesh. Second International Conference on Innovations in Management , Science , Technology and Automation in Sports ( ICIMSTAS-2023 ), 14–31. https://doi.org/10.5281/zenodo.13763573
Naveed, Q. N., Choudhary, H., Ahmad, N., Alqahtani, J., & Qahmash, A. I. (2023). Mobile Learning in Higher Education: A Systematic Literature Review. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813566
Purnadewi, G. A. A., & Widana, I. W. (2023). Improving students’ science numeration capability through the implementation of the PBL model based on local wisdom. Indonesian Journal of Educational Development (IJED), 4(3), 307-317. https://doi.org/10.59672/ijed.v4i3.3252
Sánchez, R. A., & Hueros, A. D. (2020). Motivational factors that influence the acceptance of Moodle using TAM3. Computers in Human Behavior, 102, 56–64.
Sathye, M., Goundar, S., & Bhardwaj, A. (2022). Determinants of Mobile Cloud Computing Adoption by Financial Services Firms. Journal of Information Technology Research, 15(1), 1–17. https://doi.org/10.4018/jitr.299921
Suhardita, K., Widana, I. W., Degeng, I. N. S., Muslihati, M., & Indreswari, H. (2024). Sharing behavior in the context of altruism is a form of strategy for building empathy and solidarity. Indonesian Journal of Educational Development (IJED), 5(3), 316-324. https://doi.org/10.59672/ijed.v5i3.4145
Tarhini, A., Hone, K., & Liu, X. (2017). Measuring the moderating effect of gender on e-learning acceptance in the Arab world: A structural equation modeling approach. Computers & Education, 105, 1–22.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–280.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
Wang, C., Dai, J., Zhu, K., Yu, T., & Gu, X. (2023). Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF. International Journal of Human-Computer Interaction, 0(0), 1–14. https://doi.org/10.1080/10447318.2023.2291609
Widana, I. W., Sumandya, I. W., Citrawan, I. W., Widana, I. N. S., Ibarra, F. P., Quicho, R. F., Delos Santos, M. R. H. M., Velasquez-Fajanela, J. V., & Mukminin, A. (2023a). The effect of teachers’ responsibility and understanding of the local wisdom concept on teachers’ autonomy in developing evaluation of learning based on local wisdom in a special needs school. Journal of Higher Education Theory and Practice, 23(10), 152-167. https://doi.org/10.33423/jhetp.v23i10.6189
Widana, I. W., Sumandya, I. W., Citrawan, I. W. (2023b). The special education teachers’ ability to develop an integrated learning evaluation of Pancasila student profiles based on local wisdom for special needs students in Indonesia. Kasetsart Journal of Social Sciences, 44(2), 527–536. https://doi.org/10.34044/j.kjss.2023.44.2.23
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
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