Determinants of mobile learning acceptance among grade 12 learners, their parents and teachers in the rural King Cetshwayo District
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Date
2020
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University of Zululand
Abstract
Science, Technology, Engineering, and Mathematics (STEM) is faced with challenges, resulting in learners’ poor performance at the matriculation level, in South Africa. In trying to improve learners’ performance in STEM-related subjects in grade 12, the Department of Basic Education, and other stakeholders, encouraged the use of mobile learning in the classroom. However, the adoption of mobile learning is contingent on the user’s attitude towards it. Despite the call by the Department of Basic Education to use mobile learning, very little is known about rural school STEM learners’, their teachers’, and parents’ acceptance of mobile learning.
In response to the lack of such limited and established studies in rural settings, this study proposed and used the South African Schools' Technology Acceptance Model (SASTAM) to investigate the factors that influence rural high school STEM learners’, their parents’ and teachers’ behavioural intention to use mobile learning for STEM learning. The SASTAM is based on the Technology Acceptance Model specifically, to examine significant differences between rural high school STEM learners’ and their parents’ and teachers’ acceptance of mobile learning. Identifying and understanding the factors that influence the acceptance of mobile learning is key to its successful implementation.
The study used an explanatory sequential mixed method design to investigate mobile learning technology acceptance in rural high schools in King Cetshwayo District. Stratified random sampling was used to select 550 rural high school STEM learners, their parents, and teachers to participate in the survey. The results from 417 respondents were stored as data and were analysed using partial least squares structural equation modeling (PLS-SEM). After quantitative data analysis were conducted,12 participants were selected to take part in interviews.
The SASTAM was validated using PLS-SEM. The results revealed that the variance explained by the model in the behavioural intention of learners, parents, and teachers was 44.3%, 39.7%, and 43.8% respectively. The data from the learners, teachers, and
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parents were combined and analysed and the variances in behavioural intention to use mobile learning, which was explained by the SASTAM, was 40.8%. Original Technology Acceptance Model variables (perceived attitude towards the use, perceived usefulness and perceived ease of use) had a direct influence on behavioural intention, and they also played mediating roles between the external variables (perceived social influence, perceived psychological readiness, perceived skills readiness and perceived resources) and behavioural intention to use mobile learning in a rural setting.
Multigroup analysis results showed that, for parents and learners, three paths (perceived ease of use to perceived attitude, perceived resources to perceived ease of use, and perceived social influence to perceived attitude towards the use) were significantly different. In contrast, only one path (perceived resources to perceived attitude towards the use) was significantly different for learners and teachers. However, all the paths were significant in each group, meaning that SASTAM can be used to predict the acceptance of mobile learning for rural high school STEM learners, their parents, and teachers.
The results of this study will both inform the Department of Basic Education of the factors that rural high STEM parents, learners and teachers consider important when accepting mobile learning, and advance the debate on the conceptual understanding of technology acceptance in education by refining the Technology Acceptance Model to suit the context, leading to a deeper understanding of factors that affect mobile learning acceptance in rural areas of developing countries.
Description
A thesis submitted in fulfilment of the requirement for the Degree of Doctor of Education in the department of Maths, Science And Technology Faculty of Education, at the University of Zululand, 2020.
Keywords
behavioural intention, M-learning, perceived attitude towards, STEM