However, STEM is faced with many challenges resulting in learners' poor performance at the matriculation level in South Africa. According to Bosman and Schulze (2018), this poor performance is because of the mismatch between the teaching style and the learners' learning styles in the classroom. Bosman and Schulze (2018) suggest that teachers are still using traditional face-to-face instruction, which can only cater for 20% of the class and fails to stimulate deep holistic learning experiences. On the other hand, Visser, Juan and Feza (2015) attribute this poor performance, particularly in rural areas, to lack of textbooks and learning material. Additionally, Mboweni (2014) blamed poor performance in STEM-related subjects to a high rate of learner absenteeism. Mboweni (2014) noted among others, poverty, HIV/AIDS, social grants pay out days, lack of parental involvement, teenage pregnancy and unstable family backgrounds as the leading causes of learners' absenteeism in rural areas. Based on the aforementioned studies, one can conclude that there is no effective teaching and learning of STEM-related subjects in rural areas.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.