Determinants of mobile learning acceptance for STEM education in rural areas. (January 2021)

• Record Type: Journal Article
• Authors: Mutambara, David; Bayaga, Anass
• Is Part Of: Computers & education. Volume 160(2021)
• Publication Date: 2021-01
• Subjects: STEM -- Mobile learning -- Acceptance -- Behavioural intention -- Technology acceptance model; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2020.104010
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: Science, Technology, Engineering, and Mathematics (STEM) is faced with challenges, resulting in learners' poor performance especially in rural areas. Previous studies have shown that mobile learning can be used to alleviate the challenges faced in STEM education in rural areas. Despite the opportunities that mobile learning can bring to STEM education, very little is known about high school STEM learners', their teachers', and parents' acceptance of mobile learning, particularly in rural settings. This study proposed and used the High School's Acceptance of Mobile Learning Model to investigate the factors that predict rural high school STEM learners', their parents', and teachers' behavioural intention to use mobile learning for STEM learning. The High School's Acceptance of Mobile Learning Model is based on the Technology Acceptance Model (TAM). Stratified random sampling was used to select 550 survey participants. Partial least squares structural equation modeling was used to analyse data from 417 valid questionnaires. The proposed model explained 40.8% of the variance in learners', teachers', and parents' acceptance of mobile learning. The original TAM variables (perceived attitude, perceived usefulness, and perceived ease of use) had direct relationship with behavioural intention, and they also played mediating roles between the external variables and behavioural intention. Multigroup analysis results showed that, for parents and learners, three paths were significantly different. In contrast, all paths were not statistically significant different for learners and teachers. However, all the paths were significant in each group, meaning that High School's Acceptance of Mobile Learning Model can be used to predict acceptance of mobile learning for learners, parents, and teachers. Highlights: Little is known about high school STEM learners', their teachers' and parents' acceptance of m-learning in rural areas. In response, this study proposed and used the South African schools' technology acceptance model (SASTAM). The proposed model explained 40.8% of the variance in learners', teachers' and parents' acceptance of m-learning. Multigroup analysis results showed that, for parents and learners, three paths were significantly different. The result meant that SASTAM can be used to predict acceptance of m-learning for learners, parents and teachers.