Main Article Content
This study sought to find factors that Science, Technology, Engineering, and Mathematics (STEM) students and educators in a developing country consider important when accepting mobile learning. The study developed a new model by extending the Technology Acceptance Model (TAM) using the construct perceived resources. Using stratified random sampling, a total of 160 STEM students and 100 educators were selected to participate in this study. The study employed a quantitative design where partial least squares structural equation modeling was used to examine STEM students' and educators' behavioural intention to use mobile learning. The developed model explained 74.1% of the variance in STEM students' and educators' behavioural intention to use mobile learning. Perceived resources, perceived ease of use, and perceived usefulness variables explained 54.8% of the variance in attitudes of STEM students' and educators' behavioural intention to use mobile learning. Attitude was the strongest indicator of STEM students' and educators' behavioural intention to use m-learning. The results indicated that both educators and students have a positive attitude towards mobile learning, given how important online learning is becoming nowadays. Additionally, there is no statistically significant difference between educators’ and students’ attitudes towards mobile learning. The implication is that developers of mobile learning systems should make their platforms easy to use and have more resources available for both teachers and learners to increase the overall acceptance of mobile learning in STEM subjects.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.
The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.
For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code
- Alrajawy, I., Isaac, O., Ghosh, A., Nusari, M., Al-Shibami, A. H., & Ameen, A. A. (2018). Determinants of Student’s intention to use Mobile learning in Yemeni public universities: Extending the technology acceptance model (TAM) with anxiety. International Journal of Management and Human Science, 2(2), 1-9.
- Akinbode, M., Agboola, M., Senanu, O. & Adeniji, C. (2020). Adoption and Use of Mobile Learning in Higher Education: The UTAUT Model. Association for Computing Machinery. pp. 20-26.
- Callum, K. M., Jeffrey, L. & Kinshuk, C. (2014). Factors impacting educators’ adoption of mobile learning. Journal of Information Technology Education Research, 13.
- Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J. & Walden, P. (2006). January. Adoption of mobile devices/services-searching for answers with the UTAUT. In Proceedings of the 39th annual Hawaii international conference on system sciences (HICSS’06). 6, pp. 132a-132a. IEEE.
- Chin, W. W. (Ed.), (1998). The Partial Least Squares approach for structural equation modeling. Hillsdale.’ New Jersey: Lawrence Erlbaum Associates.
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ, Lawrence Earlbaum Associates.
- Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research: Sage publications.
- Criollo-C, S., Luján-Mora, S. & Jaramillo-Alcázar, A. (2018). March. Advantages and disadvantages of M-learning in current education. In 2018 IEEE World Engineering Education Conference (EDUNINE) (pp. 1-6). IEEE.
- Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, pp.1111-1132.
- Department of Basic Education (DoE) (2003). White Paper on e-Education. Transforming Learning and Teaching through Information and Communication Technologies. Pretoria, Government Printers.
- Department of Basic Education (DoE) (2020a). Report on the 2019 national senior certificate examination. Pretoria, Government Printers.
- Department of Basic Education (DoE) (2020b). Minister Angie Motshekga on Basic Education Sector Plans to Support Students during Coronavirus COVID-19 Lockdown.
- Dutota, V., Bhatiasevib, V. & Bellallahom, N. (2019). Applying the technology acceptance model in a three-countries study of smartwatch adoption. Journal of High Technology Management Research, 30, pp.1–14.
- Estrieganaa, R., Medina-Merodiob, J.-A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education,135, pp.1-14.
- Hao, S., Dennen, V.P. & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), pp.101-123.
- Ford, M., & Botha, A. (2010). A Pragmatic Framework for Integrating ICT into Education in South Africa. In P. C. A. M. Cunningham (Ed.). IST-Africa 2010 Conference Proceedings, 2010.
- Garson, G. D. (2016). Partial Least Squares: Regression & Structural Equation Models. Asheboro, NC, USA, Statistical Publishing Associates.
- Gonzalez, H. B., & Kuenzi, J. J. (2012, August). Science, technology, engineering, and mathematics (STEM) education: A primer. Washington, DC: Congressional Research Service, Library of Congress.
- Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
- Kennedy, T. J., & Odell, M. R. L. (2014). Engaging students in STEM education. Science Education International, 25(3), 246-258.
- Kim, C., Kim, M. K., Lee, C., J. M Spector & Demeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29, pp.76-85.
- Kim, J., & Lee, K. S.-S. (2020). Conceptual model to predict Filipino educators’ adoption of ICT-based instruction in class: using the UTAUT model. Asia Pacific Journal of Education, 1-15.
- Kukulska-Hulme, Agnes, & John Traxler. (2007). Mobile teaching and learning. In Mobile learning, pp. 41-60. Routledge, 2007.
- Lin, S.H., Lee, H.C., Chang, C.T. & Fu, C.J. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, pp.101387
- Lim, W. M. (2018). Revisiting concepts and theories in information systems and technology. Australasian Journal of Information Systems, 22.
- Makgato, M. (2007). Factors associated with poor performance of students in mathematics and physical science in secondary schools in Soshanguve, South Africa. Africa Education Review, 4(1), pp.89–103.
- Mhlanga, D. & Moloi, T. (2020). COVID-19 and the Digital Transformation of Education: What Are We Learning on 4IR in South Africa? Education Sciences, 10(7), p.180.
- Modisaotsile, B.M. (2012). The failing standard of basic education in South Africa. Policy brief, 72, pp.1-7.
- Mohammadi, F. & Mahmoodi, F. (2019). Factors Affecting Acceptance and Use of Educational Wikis: Using Technology Acceptance Model (3). Interdisciplinary Journal of Virtual Learning in Medical Sciences, 10, pp 234-243.
- Montrieux, H., Grove, F. D. & Schellens, T. (2014). Mobile learning in secondary education: Educators’ and students’ perceptions and acceptance of tablet computers.’ International Journal of Mobile and Blended Learning, 6, pp.26-40.
- Mutambara, D., & Bayaga, A. (2021). Determinants of Mobile Learning Acceptance for STEM Education in Rural Areas. Computers & Education, 104010
- Odiakaosa, O. J., Dlodlo, N. & Jere, N. (2017). Teacher and Learner Perceptions on Mobile Learning Technology: A Case of Namibian Secondary schools from the Hardap Region.’ Higher Educator-An International Journal, 1, pp. 13-41.
- Osakwe, J., Dlodlo, N. & Jere, N. (2017). Where students’ and educators’ perceptions on mobile learning meet: A case of Namibian secondary schools in the Khomas region. Technology in Society, 49, pp. 16-30.
- Padayachee, K. (2017). A snapshot survey of ICT integration in South African schools. South African Computer Journal, 29(2), pp.36-65.
- Pinker, S. (1997) How the Mind Works, New York: W. W. Nortan.
- Sánchez-Prietoa, J. C., Hernández-Garcíab, Á., García-Peñalvoa, F. J., Chaparro-Peláezb, J. & Olmos-Migueláñeza, S. (2019). Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service educators. Computers in Human Behavior, 95, pp. 158–167
- Saroia, A. I. & Gao, S. (2018). Investigating university students’ intention to use mobile learning management system. Innovations in Education and Teaching International.
- Sivo, S. A., Ku, C.-H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4).
- STEM Task Force Report. (2014). Innovate: a blueprint for science, technology, engineering, and mathematics in California public education. Dublin, California: Californians Dedicated to Education Foundation.
- 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.
- Visser, Mariette, Andrea Juan, & Nosisi Feza. (2015). Home and school resources as predictors of mathematics performance in South Africa. South African Journal of Education, 35(1).
- Wong, W. H., & Mo, W. Y. (2019). A study of consumer intention of mobile payment in Hong Kong, based on perceived risk, perceived trust, perceived security and Technological Acceptance Model. Journal of Advanced Management Science Vol, 7(2), 33-38.
- Zarafshani, K., Solaymani, A., D’Itri, M., Helms, M.M. & Sanjabi, S. (2020). Evaluating technology acceptance in agricultural education in Iran: A study of vocational agriculture educators. Social Sciences & Humanities Open, 2(1), p.100041.