UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||
| Abstract : |
| This paper identifies factors affecting the adoption of mobile learning application in the classroom. The principles of the Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) were adopted as the main elements that were investigated in this study, namely relative advantage, complexity, mobile learning acceptance, and intention to use mobile learning. The research design was based on a quantitative approach using an online survey involving a group of 200 undergraduates. Data collected were analyzed using the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) on AMOS 20.0. Interestingly, the main research findings showed that all the indices fit the hypothesized model perfectly and all the technology acceptance constructs were significantly correlated. The finding encourage that UPSI’s undergraduates are perceptive to utilizing mobile learning approach with the utilize of novel mobile applications, which surely would have an enormous impact on the current teaching and learning practice in the campus. From the practical standpoint, such a learning paradigm would become more prevalent in many institutions of higher learning as mobile technology keeps on improving and becoming more affordable, hence enabling more students to gain unrivaled access to mobile online learning content. |
| References |
1. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694. 2. Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93-102. 3. Anderson, T., Varnhagen, S., & Campbell, K. (1998). Faculty adoption of teaching and learning technologies: Contrasting earlier adopters and mainstream faculty. 4. Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P. (2006, January). Adoption of mobile devices/services—searching for answers with the UTAUT. In System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 6, pp. 132a-132a). IEEE. 5. Chang, C. Y., Lai, C. L., & Hwang, G. J. (2018). Trends and research issues of mobile learning studies in nursing education: A review of academic publications from 1971 to 2016. Computers & Education, 116, 28-48. 6. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064. 7. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064. 8. Cunningham, E., Holmes-Smith, P., & Coote, L. (2006). Structural equation modeling: From the fundamentals to advanced topics. Streams Statsline, Melbourne. 9. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. 10. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. 11. Demirbilek, M. (2010). Investigating attitudes of adult educators towards educational mobile media and games in eight European countries. Journal of Information Technology Education: Research, 9, 235-247. 12. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50. 13. Furió, D., Juan, M. C., Seguí, I., & Vivó, R. (2015). Mobile learning vs. traditional classroom lessons: a comparative study. Journal of Computer Assisted Learning, 31(3), 189-201. 14. Gao, S. (2011). High level modeling and evaluation of multi-channel services. Norwegian University of Science and Technology. 15. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th ed. Uppersaddle River: Pearson Prentice Hall. 16. Irby, T. L., & Strong, R. (2015). A synthesis of mobile learning research implications: Agricultural faculty and student acceptance of mobile learning in academia. NACTA Journal, 59(1), 10. 17. Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611-630. 18. Kukulska-Hulme, A., & Traxler, J. (Eds.). (2005). Mobile learning: A handbook for educators and trainers. Psychology Press. 19. Lee, J. M., & Rha, J. Y. (2016). Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce. Computers in Human Behavior, 63, 453-462. 20. Liu, S. H. (2011). Factors related to pedagogical beliefs of teachers and technology integration. Computers & Education, 56(4), 1012-1022. 21. Liu, Y., Han, S., & Li, H. (2010). Understanding the factors driving m-learning adoption: a literature review. Campus-Wide Information Systems, 27(4), 210-226. 22. Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393-403. 23. Luo, N., Zhang, M., & Qi, D. (2017). Effects of different interactions on students' sense of community in e-learning environment. Computers & Education, 115, 153-160. 24. Mouza, C., & Barrett-Greenly, T. (2015). Bridging the app gap: An examination of a professional development initiative on mobile learning in urban schools. Computers & Education, 88, 1-14. 25. Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in health sciences education, 15(5), 625-632. 26. Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory. 27. Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of service research, 2(4), 307-320. 28. Park, Y. (2011). A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78-102. 29. Rogers, E. M., Medina, U. E., Rivera, M. A., & Wiley, C. J. (2005). Complex adaptive systems and the diffusion of innovations. The Innovation Journal: The Public Sector Innovation Journal, 10(3), 1-26. 30. Rogers, E.M. (2003). Diffusion of innovations (5th Ed.). New York: Free Press. 31. Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic commerce research and applications, 9(3), 209-216. 32. Thornton, P., & Houser, C. (2005). Using mobile phones in English education in Japan. Journal of computer assisted learning, 21(3), 217-228. 33. Trebbi, T. (2011). The potential of ICT for a new educational paradigm: Toward generalizing access to knowledge. American Journal of Distance Education, 25(3), 152-161. 34. Vavoula, G., & Sharples, M. (2009). Meeting the challenges in evaluating mobile learning: a 3-level evaluation framework. International Journal of Mobile and Blended Learning, 1, 54-75. 35. Yildirim, S., Goktas, Y., Temur, N., & Kocaman, A. (2004). A Checklist for a Good Learning Management System (LMS). Turk Egitim Bilimleri Dergisi, 4(2), 455-462. |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |