Students’ Acceptance and Perceptions of Perceived Usefulness of Mobile Learning Devices in Higher Educational Institutions

John Edumadze 1, Gopolang Ditlhokwa 2 * , John Demuyakor 2
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1 School of Philosophy, Beijing Normal University, Beijing, CHINA
2 Institute of Communication Studies, Communication University of China, Beijing, CHINA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 12, Issue 2, Article No: e202209.
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As technology-mediated innovations like Mobile Learning Devices (MLDs) spread rapidly across the globe, there are growing concerns on the actual factors that influence students in Higher Educational institutions (HEIs) to accept technology-mediated innovations like smartphones, tablets, and portable computing devices for their educational pursuit. This study adopted Technology Acceptance Model (TAM) as a theoretical basis in an attempt to investigate factors that might influence students to accept or decline the use of technology-mediated innovations specifically MLDs for academic purposes from the perspectives of three universities in Ghana. A set of online questionnaire survey was used to collect the needed data from (N=1,030) students. The researchers also conducted data analysis and presentation of findings by testing the suggested research model through Structural Equation Modelling. A regression analysis was also carried out with the help of SmartPLS to assess the path coefficient of the data collected for the model. This study identified influencing factors such as students’ awareness levels, m-learning technology types, perceived ease of use, and perceived usefulness as some of the central factors that determine how students use and accept m-learning devices in Ghanaian universities. The study reported limitations such as expensive internet data, poor internet infrastructure, insecurity, privacy issues, and unavailability of electricity as some of the factors limiting the acceptance of MLDs by students in Ghana. Despite the limitations reported in this study, the results from the statistical analysis, show that there are high levels of MLDs acceptance among students from the three sampled higher educational institutions in Ghana. The study recommends that school authorities and governments in developing countries such as Ghana incorporate MLDs in their current higher educational systems.


Edumadze, J., Ditlhokwa, G., & Demuyakor, J. (2022). Students’ Acceptance and Perceptions of Perceived Usefulness of Mobile Learning Devices in Higher Educational Institutions. Online Journal of Communication and Media Technologies, 12(2), e202209.


  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5), 83-107.
  • Ahmad, T. (2020). Student perceptions on using cell phones as learning tools: Implications for mobile technology usage in Caribbean higher education institutions. PSU Research Review, 4(1), 25-43.
  • Al-Hamad, N. Q., AlHamad, A. Q., & Al-Omari, F. A. (2020). Smart devices employment in teaching and learning: Reality and challenges in Jordan universities. Smart Learning Environments, 7(1), 5.
  • Ally, M., & Prieto-Blázquez, J. (2014). Quin és el futur de l’aprenentatge mòbil en l’educació? [What is the future of mobile learning in education?] RUSC. Revista de Universidad y Sociedad Del Conocimiento [University and Knowledge Society Magazine], 11(1), 142.
  • Alpert, F. (2016). Revitalizing the live lecture class with instructor-created videos. SAGE Open, 6(4), 215824401668068.
  • Alzaza, N. S., & Yaakub, A. R. (2011). Students’ awareness and requirements of mobile learning services in the higher education environment. American Journal of Economics and Business Administration, 3(1), 95-100.
  • Apuke, O. D., & Iyendo, T. O. (2018). University students’ usage of the internet resources for research and learning: Forms of access and perceptions of utility. Heliyon, 4(12), e01052.
  • Badwelan, A., Drew, S., & Bahaddad, A. A. (2016). Towards acceptance m-learning approach in higher education in Saudi Arabia. International Journal of Business and Management, 11(8), 12.
  • Brame, C. J. (2015). Effective educational videos. Vanderbilt University.
  • Brantes Ferreira, J., Zanela Klein, A., Freitas, A., & Schlemmer, E. (2013). Mobile learning: Definition, uses, and challenges. In L. A. Wankel, & P. Blessinger (Eds.), Cutting-edge technologies in higher education (pp. 47-82). Emerald Group Publishing Limited.
  • Cassidy, E. D., Colmenares, A., Jones, G., Manolovitz, T., Shen, L., & Vieira, S. (2014). Higher education and emerging technologies: Shifting trends in student usage. The Journal of Academic Librarianship, 40(2), 124-133.
  • Castro, J., Yamada, G., & Arias, O. (2016). Higher education decisions in Peru: On the role of financial constraints, skills, and family background. Higher Education, 72(4), 457-486.
  • Chand, P., & Arora, J. (2008). Access to scholarly communication in higher education in India: Trends in usage statistics via INFLIBNET. The Program, 42(4), 382-390.
  • Coman, C., Țîru, L. G., Meseșan-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective. Sustainability, 12(24), 10367.
  • Criollo-C, S., Lujan-Mora, S., & Jaramillo-Alcazar, A. (2018). Advantages and disadvantages of m-learning in current education. 2018 IEEE World Engineering Education Conference (pp. 1-6).
  • Davis, F. D. (1989). A technology acceptance model for empirically testing new end-user information systems: Theory and result in a doctoral dissertation. MIT Sloan School of Management.
  • Demuyakor, J. (2021). COVID-19 pandemic and higher education: Leveraging on digital technologies and mobile applications for online learning in Ghana. Shanlax International Journal of Education, 9(3), 26-38.
  • Díez-Echavarría, L., Valencia, A., & Cadavid, L. (2018). Mobile learning on higher educational institutions: How to encourage it? Simulation approach. DYNA, 85(204), 325-333.
  • Dontre, A. J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3), 379-390.
  • García-Martínez, I., Fernández-Batanero, J. M., Cobos Sanchiz, D., & Luque de la Rosa, A. (2019). Using mobile devices for improving learning outcomes and teachers’ professionalization. Sustainability, 11(24), 6917.
  • Kankam, P. K. (2020). Mobile information behaviour of sandwich students towards mobile learning integration at the University of Ghana. Cogent Education, 7(1), 1796202.
  • Kim, S. H., Mims, C., & Holmes, K. P. (2006). An introduction to current trends and benefits of mobile wireless technology use in higher education. AACE Journal, 14(1), 77-100.
  • Lai, Y.-H. (2019). The application of meta-analytic SEM on exploring factors that influence teachers’ usage of interactive whiteboard. Pedagogical Research, 4(3), em0038.
  • Letchumanan, M., & Muniandy, B. (2016). How do mathematics postgraduate students use mobile e-book? Library Hi Tech News, 33(7), 6-7.
  • Mahasneh, O. (2021). Factors that affect university college students’ acceptance and use of mobile learning (Ml). International Journal of Instruction, 14(3), 861-872.
  • Mahasneh, O. M. (2020). The effect of teaching by (mobile learning) in university students ‘achievement. Proceedings of the 6th International Conference on Mobile Learning 2020 (pp. 121-125).
  • Mahasneh, O. M., Tawarah, H. M., & Al-lawama, H. A. (2021). Using structural equation model to reveal factors affecting faculty members in university colleges in the use of Moodle. International Journal of Education and Practice, 9(1), 171-184.
  • Marquina. P. F. (2018). Developing world-class students in Peru. GlobalFocus.
  • Milošević, I., Živković, D., Manasijević, D., & Nikolić, D. (2015). The effects of the intended behavior of students in the use of m-learning. Computers in Human Behavior, 51, 207-215.
  • Mohammadi, M., Sarvestani, M. S., & Nouroozi, S. (2020). Mobile phone use in education and learning by faculty members of technical-engineering groups: Concurrent mixed methods design. Frontiers in Education, 5, 16.
  • Mpungose, C. B. (2020). The emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanities and Social Sciences Communications, 7(1), 113.
  • Mynbayeva, A., Sadvakassova, Z., & Akshalova, B. (2018). Pedagogy of the twenty-first century: Innovative teaching methods. In O. B. Cavero, & N. Llevot-Calvet (Eds.), New pedagogical challenges in the 21st century-Contributions of research in education. InTech.
  • O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, 160940691989922.
  • Okoye, K., Rodriguez-Tort, J. A., Escamilla, J., & Hosseini, S. (2021). Technology-mediated teaching and learning process: A conceptual study of educators’ response amidst the COVID-19 pandemic. Education and Information Technologies, 26, 7225-7257.
  • Ontiveros, M., & Pazos, J. R. C. (2013). Education and technology in Mexico and Latin America: Outlook and challenges. RUSC. Revista de Universidad y Sociedad Del Conocimiento [University and Knowledge Society Magazine], 10(2), 163-169.
  • Peters, K. (2007). M-learning: Positioning educators for a mobile, connected future. The International Review of Research in Open and Distributed Learning, 8(2).
  • Ragusa, A. T., & Crampton, A. (2017). Online learning: Cheap degrees or educational pluralization? Cheap degrees or educational pluralization? British Journal of Educational Technology, 48(6), 1208-1216.
  • Romero-Rodríguez, J.-M., Aznar-Díaz, I., Hinojo-Lucena, F.-J., & Cáceres-Reche, M.-P. (2020). Models of good teaching practices for mobile learning in higher education. Palgrave Communications, 6(1), 80.
  • Sarrab, M., Al-Shihi, H., & Khan, A. I. (2015). An empirical analysis of mobile learning (m-learning) awareness and acceptance in higher education. 2015 International Conference on Computing and Network Communications (pp. 960-963).
  • Sharma, S. K., Joshi, A., & Sharma, H. (2016). A multi-analytical approach to predict Facebook usage in higher education. Computers in Human Behavior, 55, 340-353.
  • Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252-275.
  • Thorsen, S. V., & Bjorner, J. B. (2010). Reliability of the Copenhagen psychosocial questionnaire. Scandinavian Journal of Public Health, 38, 25-32.
  • UNESCO. (2015, February 27). Mobile technology is the key to bringing ‘education to all’, says UN Broadband Commission. UNESCO.
  • Vázquez Cano, E., & Sevillano-García, M.a L. (2018). Ubiquitous educational use of mobile digital devices. A general and comparative study in Spanish and Latin American higher education. Journal of New Approaches in Educational Research, 7(2), 105-115.
  • Walton, G., Childs, S., & Blenkinsopp, E. (2005). Using mobile technologies to give health students access to learning resources in the UK community setting. Health Information and Libraries Journal, 22(s2), 51-65.
  • Yilmaz, Y., Suner, A., & Yilmaz, O. (2020). Mobile learning in a flipped classroom: Findings from a “5-lecture-5” blended learning design for large classes. Turkish Journal of Biochemistry, 0(0), 20190417.