Original Research

Rural-based Science, Technology, Engineering and Mathematics teachers’ and learners’ acceptance of mobile learning

David Mutambara, Anass Bayaga
SA Journal of Information Management | Vol 22, No 1 | a1200 | DOI: https://doi.org/10.4102/sajim.v22i1.1200 | © 2020 David Mutambara, Anass Bayaga | This work is licensed under CC Attribution 4.0
Submitted: 06 February 2020 | Published: 18 September 2020

About the author(s)

David Mutambara, Department of Maths, Science and Technology Education, Faculty of Education, University of Zululand, Richards Bay, South Africa
Anass Bayaga, Department of Maths, Science and Technology Education, Faculty of Education, University of Zululand, Richards Bay, South Africa


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Abstract

Background: Science, Technology, Engineering and Mathematics (STEM) is faced with many challenges resulting in learners’ poor performance at matriculation level in South Africa. However, prior research has shown that mobile learning (m-learning) can be used to alleviate the challenges of STEM education. Prior research focused on tertiary institutions’ students and lecturers, in developed countries. However, very little is known about rural school STEM teachers’ and learners’ acceptance of m-learning.

Objectives: The article investigates factors that rural-based STEM teachers and learners consider important when adopting mobile learning. Furthermore, the study also seeks to examine if there is a statistically significant difference between teachers’ and learners’ acceptance of mobile learning.

Method: The research employed a quantitative approach. Stratified random sampling was used to select 350 teachers and learners to participate in the survey. Valid questionnaires received were 288 (82%), and data were analysed using partial least squares structural equation modelling.

Results: The proposed model explained 64% of the variance in rural-based STEM teachers’ and learners’ behavioural intention to use m-learning. Perceived attitude towards use was found to be the best predictor of teachers’ and learners’ behavioural intention. The results also showed no significant difference between teachers’ and learners’ path coefficients.

Conclusion: The research recommends that awareness campaigns, infrastructure, mobile devices and data need to be made available for m-learning to be successfully adopted in rural areas.


Keywords

technology acceptance model; perceived social influence; perceived resources; STEM; perceived usefulness; perceived ease of use; perceived ease to collaborate.

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