International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)

IJCRSEE Editorial Team; Mohammed Al Masarweh; Waleed Afandi

doi:10.23947/2334-8496-2022-10-3-117-128

Authors

Mohammed Al Masarweh Management Information System Department, College of Business in Rabigh, King Abdulaziz University, Saudi Arabia https://orcid.org/0000-0001-8607-0269
Waleed Afandi Management Information System Department, College of Business in Rabigh, King Abdulaziz University, Saudi Arabia https://orcid.org/0000-0002-2175-9484

DOI:

https://doi.org/10.23947/2334-8496-2022-10-3-117-128

Keywords:

distance learning, UTAUT, higher education, m-learning, user acceptance, Saudi Arabia

Abstract

Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow researches to tested the use of m-learning for regular students while they are studying in distance learning environment. However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework was depended on the model of Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The results indicated that significant factors influencing distance learning students’ behavioral intention include quality of service, effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher Education are having a good impact and encouraging widespread use of m-learning.

Downloads

Download data is not yet available.

References

Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205-7224. https://doi.org/10.1007/s10639-021-10573-5 DOI: https://doi.org/10.1007/s10639-021-10573-5

Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82-107. https://doi.org/10.19173/irrodl.v14i5.1631 DOI: https://doi.org/10.19173/irrodl.v14i5.1631

Abu-Al-Aish, A., Love, S., Hunaiti, Z., & Al-masaeed, S. (2013). Toward a sustainable deployment of m-learning in higher education. International Journal of Mobile Learning and Organisation, 7(3-4), 253-276. https://doi.org/10.1504/IJMLO.2013.057165 DOI: https://doi.org/10.1504/IJMLO.2013.057165

Afandi, W. (2022). Saudi Higher Education Student Acceptance of Mobile Learning. International Journal of Information and Education Technology, 12(6). https://doi.org/10.18178/ijiet.2022.12.6.1647 DOI: https://doi.org/10.18178/ijiet.2022.12.6.1647

Al Masarweh, M. (2018). Evaluating M-learning in Saudi Arabia universities using concerns-based adoption model level of use framework. International Journal of Advanced Computer Science and Applications, 9(6). https://doi.org/10.14569/IJACSA.2018.090609 DOI: https://doi.org/10.14569/IJACSA.2018.090609

Al Masarweh, M. (2019). Evaluating M-Learning System Adoption by Faculty Members in Saudi Arabia Using Concern Based Adoption Model (CBAM) Stages of Concern. International Journal of Emerging Technologies in Learning, 14(5). https://doi.org/10.3991/ijet.v14i05.8296 DOI: https://doi.org/10.3991/ijet.v14i05.8296

Alahmari, A. (2017). The state of distance education in Saudi Arabia. Quarterly Review of Distance Education, 18(2), 91-98. Retrieved from https://books.google.com.sa/books?id=MfQ-DwAAQBAJ

Al-Fahad, F. N. (2009). Students’ attitudes and perceptions towards the effectiveness of mobile learning in King Saud University, Saudi Arabia. Online Submission, 8(2). Retrieved from https://files.eric.ed.gov/fulltext/ED505940.pdf

Almaiah, M. A., & Man, M. (2016). Empirical investigation to explore factors that achieve high quality of mobile learning system based on students’ perspectives. Engineering science and technology, an international journal, 19(3), 1314-1320. Retrieved from https://doi.org/10.1016/j.jestch.2016.03.004 DOI: https://doi.org/10.1016/j.jestch.2016.03.004

Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Preliminary study for exploring the major problems and activities of mobile learning system: a case study of Jordan. Journal of Theoretical & Applied Information Technology, 93(2). Retrieved from https://www.researchgate.net/publication/311790238_Preliminary_study_for_exploring_the_major_problems_and_activities_of_mobile_learning_system_A_case_study_of_Jordan

Al-Nawayseh, M.K., Baarah, A.H., Al-Masaeed, S.A. and Alnabhan, M.M. (2019). Mobile learning adoption in Jordan: Technology influencing factors. International Journal of Networking and Virtual Organisations, 20(4), 400-417. https://doi.org/10.1504/IJNVO.2019.100600 DOI: https://doi.org/10.1504/IJNVO.2019.100600

Alshurideh, M. (2010). Customer service retention–A behavioural perspective of the UK mobile market (Doctoral dissertation, Durham University).Retrieved from http://etheses.dur.ac.uk/552/

Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65-71. https://doi.org/10.1016/j.chb.2015.05.046 DOI: https://doi.org/10.1016/j.chb.2015.05.046

Anshari, M., Almunawar, M. N., Shahrill, M., Wicaksono, D. K., & Huda, M. (2017). Smartphones usage in the classrooms: Learning aid or interference?. Education and Information technologies, 22(6), 3063-3079. https://doi.org/10.1007/s10639-017-9572-7 DOI: https://doi.org/10.1007/s10639-017-9572-7

Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659-673. https://doi.org/10.1007/s10209-019-00685-8 DOI: https://doi.org/10.1007/s10209-019-00685-8

Barker, A., Krull, G., & Mallinson, B. (2005, October). A proposed theoretical model for m-learning adoption in developing countries. In Proceedings of mLearn (Vol. 2005, p. 4th). Retrieved from https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.3956

Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003 DOI: https://doi.org/10.1016/j.im.2019.05.003

Colleges, B. (2017). Online education trends report. Seattle, WA. Best Colleges.com. Last viewed: 19th of August 2022. https://cdn.website-editor.net/25dd89c80efb48d88c2c233155dfc479/files/uploaded/2017-Online-Education-Trends-Report.pdf. URL: https://www.bestcolleges.com/research/annual-trends-in-online-education/

Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1), 91-96. https://doi.org/10.1007/s11125-020-09464-3 DOI: https://doi.org/10.1007/s11125-020-09464-3

Gabriska, D., & Pribilova, K. (2021, November). Use of modern technologies and expert systems in the educational process. In 2021 19th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 126-132). IEEE. https://doi.org/10.1109/ICETA54173.2021.9726541 DOI: https://doi.org/10.1109/ICETA54173.2021.9726541

Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069 DOI: https://doi.org/10.1016/j.jbusres.2019.11.069

Jogezai, N. A., Baloch, F. A., Jaffar, M., Shah, T., Khilji, G. K., & Bashir, S. (2021). Teachers’ attitudes towards social media (SM) use in online learning amid the COVID-19 pandemic: the effects of SM use by teachers and religious scholars during physical distancing. Heliyon, 7(4), e06781. https://doi.org/10.1016/j.heliyon.2021.e06781 DOI: https://doi.org/10.1016/j.heliyon.2021.e06781

Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A. & Hall, C. (2016). NMC Horizon Report: 2016 Higher Education Edition. Austin, Texas: The New Media Consortium. Retrieved October 8, 2022 from https://www.learntechlib.org/p/171478/

Jouicha, A. I., Burgos, D., & Berrada, K. (2022, February). The Use of Mobile Learning in Higher Education: A Study on the MOOC of Cadi Ayyad University. In International Conference on Information Technology & Systems (pp. 400-425). Springer, Cham. https://doi.org/10.1007/978-3-030-96293-7_34 DOI: https://doi.org/10.1007/978-3-030-96293-7_34

Koole, M. (2006). The framework for the rational analysis of mobile education (FRAME) model: An evaluation of mobile devices for distance education (Doctoral dissertation). Retrieved from http://hdl.handle.net/2149/543

Koole, M. L. (2009). A model for framing mobile learning. Mobile learning: Transforming the delivery of education and training, 1(2), 25-47. Retrieved from https://books.google.com.sa/books?id=Itp60WteuJsC&lpg=PA25&ots=5_IQI9EPjg&dq=Koole%2C%20M.%20L.%20(2009).%20A%20model%20for%20framing%20mobile%20learning.%20Mobile%20learning%3A%20Transforming%20the%20delivery%20of%20education%20and%20training%2C%201(2)%2C%2025-47.&lr&pg=PP1#v=onepage&q&f=false

Lai, P.C., (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, pp.21-38. Retrieved from https://www.scielo.br/j/jistm/a/D3NXPz5WF4gQX9cSdLKQv6D/abstract/?lang=en

Mergany, N. N., Dafalla, A. E., & Awooda, E. (2021). Effect of mobile learning on academic achievement and attitude of Sudanese dental students: a preliminary study. BMC medical education, 21(1), 1-7. https://doi.org/10.1186/s12909-021-02509-x DOI: https://doi.org/10.1186/s12909-021-02509-x

Mohammadi, H. (2015). Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior, 49, 191-207. https://doi.org/10.1016/j.chb.2015.03.006 DOI: https://doi.org/10.1016/j.chb.2015.03.006

Mostakhdemin-Hosseini, A. (2009). Analysis of Pedagogical Considerations of M-Learning in Smart Devices. International Journal of Interactive Mobile Technologies, 3(4). http://dx.doi.org/10.3991/ijim.v3i4.855 DOI: https://doi.org/10.3991/ijim.v3i4.855

Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & education, 49(3), 581-596. https://doi.org/10.1016/j.compedu.2005.10.011 DOI: https://doi.org/10.1016/j.compedu.2005.10.011

Moya, M., Nabafu, R., Maiga, G., & Mayoka, K. (2017). Attitude and behavioral intention as mediators in adoption of e-tax services in Ura, Uganda. ORSEA JOURNAL, 6(1). Retrieved from http://www.journals.udsm.ac.tz/index.php/orsea/article/view/849/780

Moya, S., & Camacho, M. (2021). Identifying the key success factors for the adoption of mobile learning. Education and Information Technologies, 26(4), 3917-3945. https://doi.org/10.1007/s10639-021-10447-w DOI: https://doi.org/10.1007/s10639-021-10447-w

Ng, W., & Nicholas, H. (2012). A framework for sustainable mobile in schools. British Journal of Education Technology, 44(5), 1-21. https://doi.org/10.1111/j.1467-8535.2012.01359.x DOI: https://doi.org/10.1111/j.1467-8535.2012.01359.x

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139-4155. https://doi.org/10.1007/s10639-020-10157-9 DOI: https://doi.org/10.1007/s10639-020-10157-9

Pedro, L. F. M. G., Barbosa, C. M. M. D. O., & Santos, C. M. D. N. (2018). A critical review of mobile learning integration in formal educational contexts. International Journal of Educational Technology in Higher Education, 15(1), 1-15. https://doi.org/10.1186/s41239-018-0091-4 DOI: https://doi.org/10.1186/s41239-018-0091-4

Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and information technologies, 26(2), 1755-1785. https://doi.org/10.1007/s10639-020-10323-z DOI: https://doi.org/10.1007/s10639-020-10323-z

Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164. https://doi.org/10.5539/ies.v6n7p157 DOI: https://doi.org/10.5539/ies.v6n7p157

Salloum, S. A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International conference on advanced intelligent systems and informatics (pp. 469-480). Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_43 DOI: https://doi.org/10.1007/978-3-319-99010-1_43

Sarrab, M., Al-Shihi, H., & Rehman, O. (2013). Exploring major challenges and benefits of m-learning adoption. British Journal of Applied Science & Technology, 3(4), 826-839. https://doi.org/10.9734/BJAST/2013/3766 DOI: https://doi.org/10.9734/BJAST/2013/3766

Syafar, F. and Husain, H. (2017). Development of an integrated framework for successful adoption and implementation of mobile collaboration technology in Indonesian healthcare. Proceedings of the 30th IBIMA, paper, 11, 108-114.Retrieved from https://www.researchgate.net/publication/321579444_Development_of_an_Integrated_Framework_for_Successful_Adoption_and_Implementation_of_Mobile_Collaboration_Technology_in_Indonesian_Healthcare

Syafar, F., Husain, H., Ridwansyah, R., Harun, S. and Sokku, S. (2017) Key Data and Information Quality Requirements for Asset Management in Higher Education: A case Study. In The 30th International Business Information Management Association Conference. Retrieved from http://eprints.unm.ac.id/id/eprint/10600

Tan, G. W. H., Ooi, K. B., Sim, J. J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82-91. https://doi.org/10.1080/08874417.2012.11645561

Vallejo-Correa, P., Monsalve-Pulido, J. and Tabares-Betancur, M., (2021). A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes. Computer Science Review, 39, p.100335. https://doi.org/10.1016/j.cosrev.2020.100335 DOI: https://doi.org/10.1016/j.cosrev.2020.100335

Venkatesh, V., 2000. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), pp.342-365. https://doi.org/10.1287/isre.11.4.342.11872 DOI: https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540

Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of electronic commerce research, 13(2), 104.Retrieved from http://www.jecr.org/node/48

Yu-Lin Jeng, Ting-Ting Wu, Yueh-Min Huang, Qing Tan, & Stephen J. H. Yang. (2010). The Add-on Impact of Mobile Applications in Learning Strategies: A Review Study. Journal of Educational Technology & Society, 13(3), 3–11. Retrieved from http://www.jstor.org/stable/jeductechsoci.13.3.3