Research Status and Trend Analysis of Influencing Factors of Mobile Learning

DOI: https://doi.org/10.62517/jhet.202415408

Author(s)

Yuxiao Luo

Affiliation(s)

Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, China

Abstract

Since mobile learning plays the significant role in the epidemic period and post-epidemic era, exploring its influencing factors is indispensable to improve the effectiveness of mobile learning. Massive amounts of previous studies and researches at home and abroad were analyzed in this research, and it was found that previous studies on influencing factors of mobile learning mainly focus on TAM model, but ignore learners’ internal psychological cognition, which might be the most important subjective factor during the process of mobile learning. It is well known that any learning process is inseparable from learners’ psychological and cognitive activities, and mobile learning is no exception. When learners take the advantage of novel technological instruments in the process of mobile learning, stimulation will lead to changes in learners’ mindsets and organisms, which in turn brings changes in their responses and behaviors. Therefore, in the future, the combination of UTAUT model and SOR model might be beneficial for scholars to explore learners’ psychological changes in the process of mobile learning profoundly, to improve learners’ continuous willingness for mobile learning behaviors effectively, and to enhance the effectiveness of mobile learning.

Keywords

Mobile Learning; Influencing Factors; TAM Model; UTAUT Model; SOR Model

References

[1]C. S. Criollo and S. Lujan-Mora. M-learning and their potential use in the higher education: a literature review. Proceedings international conference on information systems and computer science, INCISCOS, 2018a: 268–273. [2]C. S. Criollo and S. Lujan-Mora. A SWOT analysis of bring your own devices in mobile learning. Proceedings of the 14th international conference on mobile learning, 2018b: 148–152. [3]J. Keengwe, G. Schnellert and D. Jonas. Mobile phones in education: challenges and opportunities for learning. Education and Information Technologies, 2014(2): 441–450. [4]S. H. Mahdi. Effectiveness of mobile devices on vocabulary learning: A meta-analysis. Journal of Educational Computing Research, 2017(56): 134–154. [5]A. Rezaei, N. B. Mai and A. J. Pesaranghader. Effectiveness of using English vocabulary mobile applications on ESL’s learning performance. The International Conference on Informatics and Creative Multimedia, 2013: 114–118. [6]B. Klimova. Impact of mobile learning on students’ achievement results. Education Sciences, 2019(2), 90. [7]Guo M. and Fu X. L. Exploration and research on ESP blended teaching enabled by mobile technology —— A case study of “News English: Viewing, Listening and Speaking”. Chinese Journal of ESP, 2022(04): 29-38+122-123. [8]D. Aristovnik, D. Kerzic, N. Ravselj, et al. Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 2020(20): 8438. [9]M. Kuhfeld, D. J. Condron and D. J. Downey. When does inequality grow? A seasonal analysis of racial/ethnic disparities in learning from kindergarten through eighth grade. Educational Researcher, 2021(4): 225-238. [10]R. Bradley. Measuring self-efficacy and self-regulation in online courses. College Student Journal, 2018(4): 518-530. [11]Huang J. H., Lin Y. R. and Chuang S. T. Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. Electronic Library, 2007(25): 586-599. [12]H. Akour. Determinants of mobile learning acceptance: An empirical investigation in higher education. Oklahoma State University, 2009. [13]Liu A. J., Liu Z. Q. and Chu Z. A. A case study of the acceptance level and influence factors of mobile learning in Nanjing. Open Education Research, 2013, 19(04): 104-111. [14]Zhu J. Study on influence factors of graduate students’ mobile learning based on the technology acceptance model. Shandong Normal University, 2015. [15]Jiang B. Q. Research on influencing factors of mobile learning supported by WeChat oriented to lifelong learning —— from the perspective of behavioral intention and satisfaction. East China Normal University, 2016. [16]A. Kemp, E. Palmer and P. Strelan. A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, 2019(50): 2394–2413. [17]Wu X. F. A study on users’ acceptance behavior on mobile internet usage. Beijing University of Posts and Telecommunications, 2008. [18]V. Venkatesh, J. Y. Thong and Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 2012(1): 157–178. [19]He H. M. and Zhang H. A study of the influencing factors of college students’ English mobile learning behavior based on unified theory of acceptance and use of technology. Journal of Southwest University of Science and Technology, 2019(5): 73-79. [20]Wen S. F., Zhao L. H. and Sun D. J. Research on the willingness to use mobile learning APP based on UTAUT model. Adult Education, 2019(10): 19-23. [21]A. Azawei and A. Alowayr. Predicting the intention to use and hedonic motivation for mobile learning: a comparative study in two Middle Eastern countries. Education Information Technology, 2020(2): 62-76. [22]Feng Y. H. and Zhang S. W. Research on influencing factors of mobile learning of local undergraduate students. Survey of Education, 2023(02): 12-16+26. [23]V. Venkatesh, M. G. Morris, G. B. Davis, et al. User acceptance of information technology toward a unified view. MIS Quarterly, 2003(3): 425-478. [24]Yang J. H., Peng Y. P., Wong S. H., et al. How e-learning environmental stimuli influence determinates of learning engagement in the context of COVID-19? SOR model perspective. Frontiers in Psychology, 2021(12): 1-12. [25]K. Illeris. Towards a contemporary and comprehensive theory of learning. International Journal of Lifelong Education, 2003(22): 396–406. [26]M. Tims, A. B. Bakker and D. Derks. Daily job crafting and the self-efficacy–performance relationship. Journal of Managerial Psychology, 2014(29): 490–507.