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

David Mutambara,Anass Bayaga

doi:10.4102/sajim.v22i1.1200

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.

Highlights

Science, Technology, Engineering and Mathematics (STEM) Task Force (2014:9) in America adopts the view that STEM education is far more than a ‘convenient integration of its four disciplines; rather, it encompasses ‘real-world, problem-based learning’ that integrates the disciplines through cohesive and active teaching and English learning approaches’
The results support the suggestion by Venkatesh et al (2003) who proposed that external variables of technology acceptance model (TAM) need to be identified and examined to ensure that TAM is a feasible model for the context
perceived social influence (PSI), perceived ease to collaborate (PEC) and perceived resources (PR) were added to TAM

Summary

Introduction

Technology, Engineering and Mathematics (STEM) Task Force (2014:9) in America adopts the view that STEM education is far more than a ‘convenient integration of its four disciplines; rather, it encompasses ‘real-world, problem-based learning’ that integrates the disciplines through cohesive and active teaching and English learning approaches’. 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. Mboweni (2014) blamed poor performance in STEM-related subjects to a high rate of learner absenteeism. Based on the aforementioned studies, one can conclude that there is no effective teaching and learning of STEM-related subjects in rural areas. Technology, Engineering and Mathematics (STEM) is faced with many challenges resulting in learners’ poor performance at matriculation level in South Africa. Very little is known about rural school STEM teachers’ and learners’ acceptance of m-learning

Objectives

Methods

Discussion

Conclusion