Author(s)

Zhuo Peng ¹

Affiliation(s)

¹ School of Economics, Shenzhen Polytechnic, Shenzhen 518055, Guangdong, China

Corresponding Author

ABSTRACT

Technology-enhanced education has become a mix-up of electronic and mobile learning since mobile information technology, mobile devices, and mobile teaching resources are popularized among teachers and students in higher education institutions. Catering to such changes, teachers are adapting themselves to employing mobile technology in curricula design, course instruction and interaction with the learners, thus enabling mobile teaching to become new instructional practices in higher education. Nevertheless, little is known about what drives university teachers to adopt mobile technology systems and the catalysts for mobile teaching behavior in higher education. This study approaches university teachers as users of mobile information technology and explores the key factors that influence their mobile teaching intention and implementation of mobile teaching. A mobile teaching acceptance model is constructed based on the unified theory of acceptance and use of technology (UTAUT) model to conceptualize mobile teaching behavior and analyzes the driving forces for university teachers to accept and adopt mobile teaching. A measurement scale for mobile teaching intention is developed, and a survey (n=389) is conducted among teachers from a polytechnic and a university in two cities. The Smart-PLS data analysis supports that university teachers' "Performance Expectancy, Task Adaptivity, Social influence" are key determinants for mobile teaching behavior intention and actual adoption of mobile information technology in teaching practices. "Effort Expectancy and Facilitating Conditions" do not significantly impact mobile teaching behavioral intention and behavior adoption.

KEYWORDS

CITE THIS PAPER

Zhuo Peng, An Empirical Study of Mobile Teaching: Applying the UTAUT Model to Study University Teachers' Mobile Teaching Behavior. Curriculum and Teaching Methodology (2022) Vol. 5: 55-70. DOI: http://dx.doi.org/10.23977/curtm.2022.050510.

REFERENCES

[1] Luo, J. (2014). Information technology drives learning changes—From classroom learning to virtual learning, mobile learning and ubiquitous learning. China Educational Technology, 324, 15-22. 
[2] Wong, L. H., & Looi, C. K. (2011). What seams do we remove in mobile assisted seamless learning? A critical review of the literature. Computers & Education, 57(4), 2364-2381. 
[3] Hwang, G.J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031.
[4] France, D., Lee, R., Maclachlan, J., & McPhee, S. R. (2021). Should you be using mobile technologies in teaching? Applying a pedagogical framework. Journal of Geography in Higher Education, 45(2), 221-237.
[5] Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance. MIS Quarterly, 13, 319-340.
[6] Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2011). Mobile learning technologies for education: benefits and pending issues. Appl. Sci., 11, 4111. 
[7] Criollo-C, S., Lujan-Mora, S, & Jaramillo-Alcazar, A. (2018). Advantages and disadvantages of M-learning in current education. Proceedings of the 2018 IEEE World Engineering Education Conference (EDUNINE), 1-6.
[8] Criollo-C, S., & Luján-Mora, S. (2017). M-Learning and their potential use in the higher education: A literature review. Proceedings of the 2017 International Conference on Information Systems and Computer Science (INCISCOS), 268–273.
[9] Motiwalla, L. F. (2007). Mobile learning: a framework and evaluation. Computers and Education, 49, 581-596.
[10] Moodley, K., Callaghan, Fraser, W. J., & Graham, M. A. (2020). Factors enhancing mobile technology acceptance: A case study of 15 teachers in a Pretoria secondary school. South African Journal of Education, 40(2), 1-16.
[11] Shraim, K., & Crompton, H. (2015). Perceptions of using smart mobile devices in higher education teaching: A case study from Palestine. Contemporary Educational Technology, 6, 301–318.
[12] Siau, K., Lim, E., & Shen, Z. X. (2001). Mobile Commerce: promises, challenges and research agenda. The Journal of Database Management, 12, 3-8. 
[13] Yu, S. Q. (2003). Mobile learning: A new frontier in E-learning. Distance Education in China, 22, 76-78.
[14]  Koole, M., & Ally, M. Framework for the Rational Analysis of Mobile Education (FRAME) Model: Revising the ABCs of Educational Practices. International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL'06).
[15]  Zhang, X. (2022). The Influence of Mobile Learning on the Optimization of Teaching Mode in Higher Education. Wireless Communications and Mobile Computing, 1-9.
[16] Perkins, S., & Saltsman, G. (2010). Mobile Learning at Abilene Christian University: Successes, Challenges, and Results from Year One. Journal of the Research Center for Educational Technology, 6(1), 47-54.
[17]  Persson, V., & Nouri, J. (2018). A systematic review of second language learning with mobile technologies. International Journal of Emerging Technologies in Learning, 13(2), 188–210. 
[18] Chen, M.L. (2022). The Impact of Mobile Learning on the Effectiveness of English Teaching and Learning—A Meta-Analysis. IEEE Access, 10.
[19] Chen, F. H., Looi, C. K., & Chen, W. (2009). Integrating technology in the classroom: A visual conceptualization of teachers’ knowledge, goals and beliefs. Journal of Computer Assisted Learning, 25(5), 470–488.
[20]  Tang, K.Y., Hsiao, C.H., Tu, Y.F., Hwang, G.J., & Wang, Y.M. Factors influencing university teachers use of a mobile technology-enhanced teaching (MTT) platform. Educational technology research and development: ETR & D, 1–24. Advance online publication.https://doi.org/10.1007/s11423-021-10032-5. 
[21]  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.
[22] Mittal, N., & Alavi, S. (2020). Construction and psychometric analysis of teachers mobile learning acceptance questionnaire. Interactive Technology and Smart Education, 17(2), 171–196.
[23] Hu, S.L., Laxman, K.,& Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies, 25(5), 4615–4635. 
[24] Wang, Z. (2019) Study on learning precautions signals based on mobile teaching APPs. Liaoning University. 
[25] Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27,425-478.
[26] Mary Helen Fagan (2019) Factors Influencing student acceptance of mobile learning in higher education. Computers in the Schools, 36(2), 105-121, 
[27] Venkatesh, V., Sykes, T.  A., & Zhang, X. (2011). ‘Just what the doctor ordered’: A revised UTAUT for EMR system adoption and use by doctors. Proceedings of the 44th Hawaii International Conference on System Sciences (HICSS), 1-10. 
[28] Gao, F. (2012). Faculty adoption and utilization of online instruction in higher education: A study based on Unified Theory of Acceptance and Use of Technology. Open Education Research, 18,106-112.
[29] Zhang, S., Liu, Q. T., Huang, J. X., & Wu, P. (2016). A study of the factors that affect Web-based learning Places use-A UTAUT model analysis. Instruction and Teacher Professional Development, 350, 99-106.
[30] Li, H. X., Zhao, C., Jiang, Z., & Liang, Y. (2017). A study on the influencing factors about preschool teachers’ acceptance of information teaching based on the UTAUT model. Preschool Education Research, 268, 14-25.
[31] Sumak, B., & Sorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre- and post-adopters. Computers in Human Behavior, 64, 602-640.
[32] Xu, L., & Zheng, Q. H. (2013). An empirical study of factors influencing college students’ adoption of mobile learning. Modern Distance Education Research, 4, 61-66.
[33] Hashim, H., Yunnus, M., & Embi, M.A. (2016). Pre-University English as second Language (ESL) learners’ attitude towards mobile learning. Creative Education, 7, 1147-1153.
[34] Griffin, M. A., Neal, A., & Parker, S. K. (2007). A new model of work role performance: positive behavior in uncertain and interdependent contexts. Academy of Management Journal, 50(2), 327-347.
[35] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R.E. (2010). Multivariate Data Analysis. 7th Edition, Pearson, New York.
[36] Wu, Q. L., & Zhang, P. Z. (2018). E-learning user acceptance model in business schools based on UTAUT in the background of internet plus. Journal of Shanghai Jiaotong University, 52, 233-241.
[37] Hussain, S., Zhu, F., Siddiqi, A. F., Ali, Z. and Shabbir, M. S. (2018) Structural equation model for evaluating factors affecting quality of social infrastructure projects. Sustainability, 10,1415
[38] Ringle, C. M., Wende, S., & Becker, J. (2015). SmartPLS 3. SmartPLS GmbH, Boenningstedt. 
[39] Gefen, D., & Straub, D. (2005). A Practical guide to factorial validity using PLS-Graph: tutorial and annotated example. Communications of the Association for Information Systems, 16, 91-110.
[40] Xu, L., & Zheng, Q. H. (2013). An empirical study of factors influencing college students’ adoption of mobile learning. Modern Distance Education Research, 4, 61-66.

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