A Investigate of Influence Factor for Tertiary Students’ M-learning effectiveness: Adjust Industry 4.0 & 12-Year Curriculum of Basic Education

Abstract

Mobile learning M-learning as a technology teaching has significant potential to improve student application and comprehension skills. The rapid development of the Internet and the resulting trends applying information available on the Internet have changed the nature of learning and learning behavior patterns of tertiary students. However, insufficient theoretical and empirical research on the effect of M-learning on learning attitude and M-learning effectiveness has been conducted for achieving any reliable understanding of the use of M-learning by tertiary students. This, study was therefore based on the theory of planned behavior, and combined the technology acceptance model and the structural equation model SEM ; it involved 892 tertiary student participants, and developed an empirical research model. The study found that in terms of M-learning acceptance, tertiary students have a positive evaluation and perception of using M-learning, with perceived enjoyment being the most significant factor. In addition, the impact of perceived innovation was significant in helping teachers understand students’ learning outcomes. The teaching material of learning motivations was also significant, indicating that current tertiary students are more likely to value their friends as sources of information. External influences on self-efficacy were also significant, suggesting that teachers must consider the ability of M-learning technology, with particular attention paid to students’ competency with the technology itself, as difficulty of use will reduce students’ willingness to use M-learning.

Keywords

• Technology acceptance model,M-learning,Tertiary students,Industry 4.0

Kaynakça

1. Al-Emran, M., Elsherif, H. M., and Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. doi:10.1016/j.chb.2015.11.033.
2. Al-Emran, M., Mezhuyev, V., and Kamaludin, A. (2018a). PLS-SEM in information systems research: A comprehensive methodological reference. 4th international conference on advanced intelligent systems and informatics (AISI 2018)Springer (in press).
3. Al-Emran, M., Mezhuyev, V., and Kamaludin, A. (2018b). Students' perceptions towards the integration of knowledge management processes in M-learning systems: A preliminary study. International Journal of Engineering Education, 34(2), 371–380.
4. Bacca, S., Baldiris, R., Fabregat, Graf, S., and Kinshuk, K. (2014). Augmented reality trends in education: A systematic review of research and applications. Educational Technology and Society, 17(4), 133–149.
5. Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
6. Bakhsh, M., Mahmood, A., and Sangi, N. A. (2017). Examination of factors influencing students and faculty behavior towards m-M-learning acceptance: An empirical study. International Journal of Information and Learning Technology, 34(3), 166–188. doi:10.1108/IJILT-08-2016-0028
7. Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., and García-Peñalvo, F. J. (2017). Learning with mobile technologies – students' behavior. Computers in Human Behavior, 72, 612–620. doi:10.1016/j.chb.2016.05.027.
8. Buabeng-Andoh, C. (2018). New technology in health education: Nursing students' application of mobile technology in the classroom in Ghana. Interactive Technology and Smart Education, 15(1), 46-58. doi:10.1108/ITSE-09-2016-0039.

9. Baturay, M. H., Gökçearslan, Ş., and Ke, F. (2017). The relationship among pre-service teachers computer competence, attitude towards computer-assisted education, and intention of technology acceptance. International Journal of Technology Enhanced Learning, 9(1), 1–13. doi:10.1504/IJTEL.2017.10003119
10. Baydas, O., and Goktas, Y. (2017). A model for preservice teachers' intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930–945. doi:10.1080/10494820.2016.1232277.
11. Chiu, P. H. P., and Cheng, S. H. (2017). Effects of active learning classrooms on student learning: a twoyear empirical investigation on student perceptions and academic performance. Higher Education Research and Development, 36(2), 269–279.
12. Chang, W.H., Liu, Y.C., and Huang, T.H. (2017). Perceptions of learning effectiveness in M‐learning: scale development and student awareness. Journal of Computer Assisted Learning, 33(5), 461- 472. doi:10.1111/jcal.12192
13. Chung, C.J., Hwang, G.J., and Lai, C.L. (2019). A review of experimental mobile learning research in 2010- 2016 based on the acitvity theory framework. Computers and Education, 129, 1-13.
14. Davenport, C. E. (2018). Evolution in student perceptions of a flipped classroom in a computer programming course. Journal of College Science Teaching, 47(4), 30–35.
15. Day, L. J. (2018). A gross anatomy flipped classroom effects performance, retention, and higher‐level thinking in lower performing students. American Association of Anatomists, 11(6), 565-574. doi:10.1002/ase.1772.
16. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis (5th ed.). New York: Macmillan
17. Hao, S., Dennen, V. P., and Mei, L. (2017). Influential factors for mobile M-learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123.
18. Hamidi, H., and Chavoshi, A. (2017). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the university of technology. Telematics and Informatics, 35(4), 1053-1070. doi:10.1016/j.tele.2017.09.016.
19. Harchay, A., Cheniti-belcadhi, L., and Braham, R. (2017). MobiSWAP: Personalized mobile assessment tool based on semantic web and web services. 2017 IEEE/ACS 14th international conference on computer systems and applications (pp. 1406–1413). IEEE.
20. Hew, K. F., Qiao, C., and Tang, Y. (2018). Understanding student engagement in large-scale open online courses: A machine learning facilitated analysis of student's reflections in 18 highly rated MOOCs. International Review of Research in Open and Distance Learning, 19(3), 69-93.
21. Hone, K. S., and El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers and Education, 98, 157–168.
22. Hsu, L. (2016). Examining EFL teachers' technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach. Computer Assisted Language Learning, 29(8), 1287–1297. doi:10.1080/09588221.2016.1278024.
23. Iqbal, S., and Bhatti, Z. A. (2017). What drives M-learning? An empirical investigation of university student perceptions in Pakistan. Higher Education Research and Development, 36(4), 730–746. doi:10.1080/07294360.2016.1236782.
24. Jeno, L.M., Vandvik, V., Eliassen, S., and Grytnes, J.A. (2019). Testing the novelty effect of an m-lerning tool on internalizaiton and achievement: A self-determination theory approach. Computers and Education, 128, 398–413.
25. Joo, Y. J., Kim, N., and 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. doi:10.1007/s11423-016-9436-7
26. Leong, L. W., Ibrahim, O., Dalvi-Esfahani, M., Shahbazi, H., and Nilashi, M. (2018). The moderating effect of experience on the intention to adopt mobile social network sites for pedagogical purposes: An extension of the technology acceptance model. Education and Information Technologies, 1–22. doi:10.1007/s10639- 018-9726-2
27. Liu, D., and Guo, X. (2017). Exploring gender differences in acceptance of mobile computing devices among college students. Information Systems and e-business Management, 15(1), 197–223. doi:10.1007/s10257-016- 0315-x
28. Nicol, A. A., Owens, S. M., Le Coze, S. S., MacIntyre, A., and Eastwood, C. (2017). Comparison of high technology active learning and low-technology active learning classrooms. Active Learning in Higher Education, 1–13.
29. Nikou, S. A., and Economides, A. A. (2017a). Mobile-based Assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95. doi:10.1016/j.chb.2016.11.020.
30. Nikou, S. A., and Economides, A. A. (2017b). Mobile-based assessment: Investigating the factors that influence doi:10.1016/j.compedu.2017.02.005. intention to use. Computers and Education, 109, 56–73.
31. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., and García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. doi:10.1016/j.chb.2016.09.061.
32. Sarrab, M., Al Shibli, I., and Badursha, N. (2016). An empirical study of factors driving the adoption of mobile learning in omani higher education. International Review of Research in Open and Distance Learning, 17(4), 331–349. doi:10.19173/irrodl.v17i4.2614
33. Siddiq, F., Scherer, R., and Tondeur, J. (2016). Teachers' emphasis on developing students' digital information and communication skills (TEDDICS): A new construct in 21st century education. Computers and Education, 92–93, 1–14. doi:10.1016/j.compedu.2015.10.006.
34. Seufert, T., Wagner, F., and Westphal, J. (2017). The effects of different levels of disfluency on learning outcomes and cognitive load. Instructional Science, 45(2), 221–238.
35. Shroff, R.H., Ting, F.S.T., and Lam, W.H. (2019). Development and validation of an instrument to measure students’ perceptions of technology-enabled active learning. Australasian Journal of Educational Technology, 35(4),109-127.
36. Tawfik, A. A., Giabbanelli, P. J., Hogan, M., Msilu, F., Gill, A., and York, C. S. (2018). Effects of success v failure cases on learner-learner interaction. Computers and Education, 118, 120–132.
37. Wai, I. S. H., Ng, S. S. Y., Chiu, D. K. W., Ho, K. K. W., and Lo, P. (2018). Exploring undergraduate students' usage pattern of mobile apps for education. Journal of Librarianship and Information Science, 50(1), 34–47. doi:10.1177/0961000616662699.
38. Yoon, H.-Y. (2016). User acceptance of mobile library applications in academic Libraries: An application of the technology acceptance model. The Journal of Academic Librarianship, 42(6), 687–693. doi:10.1016/j.acalib.2016.08.003.
39. Zhang, J., Chang, C., and Zhou, P. (2015). Factors affecting the acceptance of mobile devices in the classroom. Educational innovation through technology (EITT), 2015 international conference (pp. 294– 298). IEEE. https://doi.org/10.1109/EITT.2015.67
40. Zhang, M., Yin, S., Luo, M., and Yan, W. (2017). Learner control, user characteristics, platform difference, and their role in adoption intention for MOOC learning in China. Australasian Journal of Educational Technology, 33(1), 114–133.