Students’ perceptions of using mobile technologies in informal English learning during the COVID-19 epidemic: A study in Chinese rural secondary schools
Jiayi Guo 1, Fang Huang 1 * , Yongqiang Lou 1, Shaomei Chen 1
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1 Foreign Languages Education Research Center, School of Foreign Languages, Qingdao University, China
* Corresponding Author


During the COVID-19 quarantine period, mobile technologies have been greatly promoted to be used by students to assist their learning. Although mobile learning has been well researched, studies investigating rural school students’ intentions were not enough. This study investigated rural secondary school students’ mobile technology uptake and their perceptions of using mobile technologies in informal English learning during the COVID-19 epidemic. Results suggested Chinese rural school students held positive attitudes towards mobile assisted English learning, and they most tended to use smartphones, followed by portable electronic dictionaries, tablets and laptops in informal English learning. Rural secondary school students’ behavioral intentions (BI) were significantly influenced by perceived usefulness (PU), facilitating conditions (FC) and attitude towards use (ATU) of mobile devices. Innovativeness (INNO) and perceived ease of use (PEU) did not significantly influence students’ behavioral intentions. These factors explained 83.1% of variance of students’ behavioral intentions. Based on the findings, the study offers suggestions that teachers, governments and educational policy makers take measures to pay attention to students’ mobile learning in informal English learning. 



  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
  • Aliaga, M., & Gunderson, B. (2002). Interactive statistics. Virginia: Pearson Education.
  • Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students’ perspectives. Journal of Pedagogical Sociology and Psychology, 2(1), 45-51.
  • Banister, S. (2010). Integrating the iPod touch in K–12 education: Visions and vices. Computers in the Schools, 27(2), 121-131.
  • Burston, J. (2014). MALL: The pedagogical challenges. Computer Assisted Language Learning, 27(4), 344-357.
  • Barhoumi, C. (2015). The effectiveness of Whats app mobile learning activities guided by activity theory on students’ knowledge management. Contemporary Educational Technology, 6(3), 221-238.
  • Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK-12 education: A ystematic review. Computers & Education, 110, 51-63.
  • China Internet Network Information Center. (2020). The 45th China statistical report on internet development. Retrieved September 20, 2020, from 202004/P020200428596599037028.pdf
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
  • Dashtestani, R. (2013). EFL teachers’ and students’ perspectives on the use of electronic dictionaries for learning English. CALL-EJ, 14(2), 51-65.
  • Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61.
  • Groves, M. M., & Zemel, P. C. (2000). Instructional technology adoption in higher education: An action research case study. International Journal of Instructional Media, 27(1), 57–65.
  • Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34, 177-189.
  • Hoy, W. K., & Adams, C. M. (2015). Quantitative research in education: A primer. New York: Sage Publications.
  • Huang, F. & Teo, T. (2020). Influence of teacher-perceived organisational culture and school policy on chinese teachers’ ıntention to use technology: An extension of technology acceptance model. Educational Technology Research & Development, 68, 1547–1567.
  • Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
  • Kinash, S., Brand, J., & Mathew, T. (2012). Challenging mobile learning discourse through research: Student perceptions of blackboard mobile learn and iPads. Australasian Journal of Educational Technology, 28(4), 639-655.
  • Khlaisang, J., Teo, T., & Huang, F. (2019). Acceptance of a flipped smart application for learning: a study among Thai university students. Interactive Learning Environments, 1-18.
  • Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., ... & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194.
  • Levy, M., & Kennedy, C. (2005). Learning Italian via mobile SMS. In A. Kukulska-Hulme, J. Traxler (Eds.) Mobile learning: A handbook for educators and trainers (pp. 76-83). London: Taylor & Francis.
  • Lefever, S., Dal, M., & Matthiasdottir, A. (2007). Online data collection in academic research: Advantages and limitations. British Journal of Educational Technology, 38(4), 574-582.
  • Naciri, A., Baba, M. A., Achbani, A., & Kharbach, A. (2020). Mobile learning in higher education: Unavoidable alternative during COVID-19. Aquademia, 4(1), ep20016.
  • Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon, 9(5), 1-6.
  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British journal of educational technology, 43(4), 592-605.
  • Symonds, P. M. (1924). On the loss of reliability in ratings due to coarseness of the scale. Journal of Experimental Psychology, 7, 456-461.
  • Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312.
  • Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3-18.
  • Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460-475.
  • Thomas, T. D., Singh, L., & Gaffar, K. (2013). The Utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology, 9(3), 71-87.
  • Yu, S., Wang, M., & Che, H. (2005). An exposition of the crucial issues in China's educational informatization. Educational Technology Research and Development, 53(4), 88-101.
  • Yang, S. H. (2012). Exploring college students' attitudes and self-efficacy of mobile learning. Turkish Online Journal of Educational Technology-TOJET, 11(4), 148-154.
  • Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending Classes Without Stopping Learning: China’s Education Emergency Management Policy in the COVID-19 Outbreak. Journal of Risk and Financial Management, 13(3), 1-6.


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