Factors Affecting Pre-service Teachers’ Acceptance of Online Learning to Promote Social Distancing

Admire Chibisa; Duduzile Christine Sibaya; David Mutambara

doi:10.25159/2663-5895/12229

Authors

Admire Chibisa University of Zululand https://orcid.org/0000-0003-3615-4645
Duduzile Christine Sibaya University of Zululand https://orcid.org/0000-0002-9822-3987
David Mutambara University of Zululand https://orcid.org/0000-0002-7953-0172

DOI:

https://doi.org/10.25159/2663-5895/12229

Keywords:

Social distancing, Coronavirus, Technology Acceptance Model, online learning, Pre-service Teachers

Abstract

The outbreak of the novel Coronavirus disease (COVID-19), which was declared a global pandemic on 11 March 2020, has upended the world. To combat its spread, social distancing was adopted as recommended by health professionals and the higher education institutions (HEIs) were not spared. Despite the continuation of the academic agenda, social distancing forced lecturers and their students apart. As a result, the goal of this study was to examine factors that influence pre-service teachers’ acceptance of online learning to promote social distancing. The study used a quantitative design, with data gathered from 163 pre-service teachers. It was underpinned by the Technology Acceptance Model (TAM). Partial Least Squares–Structural Equation Modelling (PLS–SEM) was used to test the hypothesised model using SmartPLS version 3.2.8 in the analysis. The model identified six factors that predict pre-service teachers’ acceptance of online learning, with a variance of 66.8% in behavioural intention to use online learning. This means that the six factors were good predictors of pre-service teachers’ acceptance of online learning to promote social distancing. Pre-service teachers’ perceived attitude towards the use of online learning plays a key role in their acceptance of online learning given its explained variance of 54.7%. As a result, in order for online learning to properly promote social distancing, the Department of Higher Education and Training (DHET) should focus more on the factors that improve pre-service teachers’ attitude towards using it.

References

Agbade, O. P., and U. T. Beyin. 2021. “Promoting the Practice of Social Distancing in Nigerian Tertiary Institutions during COVID-19 Era: A Fundamental Public Health Measure for Containing the Pandemic on Campus.” Journal of Educational Realities 11 (1): 11 pp.

Aldheleai, Y., M., R. Baki, Z. Tasir, and W. Alrahmi. 2019. “What Hinders the Use of ICT among Academic Staff at Yemen’s Public Universities?” International Journal of Humanities and Innovation 2 (1): 7–12. https://doi.org/10.33750/ijhi.v2i1.30 DOI: https://doi.org/10.33750/ijhi.v2i1.30

Alrajawy, I. M., O. Isaac, A. Ghosh, M. Nusari, A. H. Al-Shibami, and A. A. Ameen. 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.

Alshmrany, S., and B. Wilkinson. 2017. “Factors Influencing the Adoption of ICT by Teachers in Primary Schools in Saudi Arabia.” International Journal of Advanced Computer Science and Applications 8 (12): 143–156. https://10.14569/IJACSA.2017.081218 DOI: https://doi.org/10.14569/IJACSA.2017.081218

Camilleri, M. A., and A. C. Camilleri. 2020. “The Use of Mobile Learning Technologies in Primary Education.” In Cognitive and Affective Perspectives on Immersive Technology in Education, edited by R. Zheng, 250–266. Hershey: IGI Global. https://doi.org/10.4018/978-1-7998-3250-8.ch013 DOI: https://doi.org/10.4018/978-1-7998-3250-8.ch013

Chang, S. L., N. Harding, C. Zachreson, O. M. Cliff, and M. Prokopenko. 2020. “Modelling Transmission and Control of the COVID-19 Pandemic in Australia.” Nature Communications 11 (1): 1–13. https://doi.org/10.1038/s41467-020-19393-6 DOI: https://doi.org/10.1038/s41467-020-19393-6

Cheng, E. W. L. 2019. “Choosing between the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM).” Educational Technology Research and Development 67 (1): 21–37. https://doi.org/10.1007/s11423-018-9598-6 DOI: https://doi.org/10.1007/s11423-018-9598-6

Chin, W. W. 1998. “The Partial Least Squares Approach to Structural Equation Modeling.” Modern Methods for Business Research 295 (2): 295–336.

Chiu, T. K. F., and D. Churchill. 2016. “Adoption of Mobile Devices in Teaching: Changes in Teacher Beliefs, Attitudes and Anxiety.” Interactive Learning Environments 24 (2): 317–327. https://doi.org/10.1080/10494820.2015.1113709 DOI: https://doi.org/10.1080/10494820.2015.1113709

Cohen, J. 2013. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hoboken: Taylor & Francis. https://doi.org/10.4324/9780203771587 DOI: https://doi.org/10.4324/9780203771587

Creswell, J. W., and C. N. Poth. 2016. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks: SAGE.

Davis, F. D., R. P. Bagozzi, and P. R. Warshaw. 1989. “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models.” Management Science 35 (8): 982–1003. https://doi.org/10.1287/mnsc.35.8.982 DOI: https://doi.org/10.1287/mnsc.35.8.982

Garson, G. D. 2016. Partial Least Squares: Regression and Structural Equation Models. Asheboro: Statistical Associates.

Greenstone, M., and V. Nigam. 2020. “Does Social Distancing Matter?” University of Chicago, Becker Friedman Institute for Economics Working Paper (2020–26). https://doi.org/10.2139/ssrn.3561244 DOI: https://doi.org/10.2139/ssrn.3561244

Hair Jr, J. F., M. Sarstedt, C. M. Ringle, and S. P. Gudergan. 2017. Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks: SAGE.

Ku, C-H. 2009. “Extending the Technology Acceptance Model Using Perceived User Resources in Higher Education Web-based Online Learning Courses.” PhD diss., University of Central Florida.

Levin, T., and R. Wadmany. 2008. “Teachers’ Views on Factors Affecting Effective Integration of Information Technology in the Classroom: Developmental Scenery.” Journal of Technology and Teacher Education 16 (2): 233–263.

Lim, W. M. 2018. “Dialectic Antidotes to Critics of the Technology Acceptance Model: Conceptual, Methodological, and Replication Treatments for Behavioural Modelling in Technology-Mediated Environments.” Australasian Journal of Information Systems 22. https://doi.org/10.3127/ajis.v22i0.1651 DOI: https://doi.org/10.3127/ajis.v22i0.1651

MacCallum, K., and L. Jeffrey. 2014. “Comparing the Role of ICT Literacy and Anxiety in the Adoption of Mobile Learning.” Computers in Human Behavior 39: 8–19. https://doi.org/10.1016/j.chb.2014.05.024 DOI: https://doi.org/10.1016/j.chb.2014.05.024

Mutambara, D., and A. Bayaga. 2020. “Predicting Rural STEM Teachers’ Acceptance of Mobile Learning in the Fourth Industrial Revolution.” Journal of Construction Project Management and Innovation 10 (2): 14–29. https://doi.org/10.36615/jcpmi.v10i2.404 DOI: https://doi.org/10.36615/jcpmi.v10i2.404

Nikou, S. A., and A. A. Economides. 2018. “Mobile‐based Micro‐learning and Assessment: Impact on Learning Performance and Motivation of High School Students.” Journal of Computer Assisted Learning 34 (3): 269–278. https://doi.org/10.1111/jcal.12240 DOI: https://doi.org/10.1111/jcal.12240

Osakwe, J. O., D. Nomusa, and N. Jere. 2017. “Teacher and Learner Perceptions on Mobile Learning Technology: A Case of Namibian High Schools from the Hardap Region.” Online Submission 1 (1): 13–41. https://doi.org/10.26762/he.2017.30000002 DOI: https://doi.org/10.26762/he.2017.30000002

Padmanathan, Y., and L. N. Jogulu. 2018. “Mobile Learning Readiness among Malaysian Polytechnic Students.” Journal of Information System and Technology Management 3 (8): 113–125.

Pinker, S. 1997. “Words and Rules in the Human Brain.” Nature 387 (6633): 547–548. https://doi.org/10.1038/42347 DOI: https://doi.org/10.1038/42347

Pullen, D., K. Swabey, M. Abadooz, and T. K. Ranjit Sing. 2015. “Pre-service Teachers’ Acceptance and Use of Mobile Learning in Malaysia.” Australian Educational Computing 30 (1): 1–14.

Sánchez-Prieto, J. C., Á. Hernández-García, F. J. García-Peñalvo, J. Chaparro-Peláez, and S. Olmos-Migueláñez. 2019. “Break the Walls! Second-order Barriers and the Acceptance of mLearning by First-year Pre-service Teachers.” Computers in Human Behavior 95: 158–167. https://doi.org/10.1016/j.chb.2019.01.019 DOI: https://doi.org/10.1016/j.chb.2019.01.019

Schulze, S., and A. Bosman. 2018. “Learning Style Preferences and Mathematics Achievement of Secondary School Learners.” South African Journal of Education 38 (1): 1–8. https://doi.org/10.15700/saje.v38n1a1440 DOI: https://doi.org/10.15700/saje.v38n1a1440

Sivo, S. A., C-H. Ku, and P. Acharya. 2018. “Understanding How University Student Perceptions of Resources Affect Technology Acceptance in Online Learning Courses.” Australasian Journal of Educational Technology 34 (4): a2806. https://doi.org/10.14742/ajet.2806 DOI: https://doi.org/10.14742/ajet.2806

Siyam, N. 2019. “Factors Impacting Special Education Teachers’ Acceptance and Actual Use of Technology.” Education and Information Technologies 24 (3): 2035–2057. https://doi.org/10.1007/s10639-018-09859-y DOI: https://doi.org/10.1007/s10639-018-09859-y

Taat, M. S., and A. Francis. 2020. “Factors Influencing the Students’ Acceptance of E-Learning at Teacher Education Institute: An Exploratory Study in Malaysia.” International Journal of Higher Education 9 (1): 133–141. https://doi.org/10.5430/ijhe.v9n1p133 DOI: https://doi.org/10.5430/ijhe.v9n1p133

Teo, T. 2008. “Pre-service Teachers’ Attitudes towards Computer Use: A Singapore Survey.” Australasian Journal of Educational Technology 24 (4): 413–424. https://doi.org/10.14742/ajet.1201 DOI: https://doi.org/10.14742/ajet.1201

Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly 27 (3): 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540