Teachers and technology: Analysing online teaching acceptance among rural educators using the Technology Acceptance Model
DOI:
https://doi.org/10.15282/ijhtc.v11i2.14238Keywords:
Technology Acceptance Model, online teaching, Attitude, rural teachers, instructional technologyAbstract
This study was conducted to identify the level of teachers’ acceptance of online teaching based on the Technology Acceptance Model (TAM). While previous studies have predominantly focused on students or urban settings, limited attention has been given to teachers in rural educational contexts. The study focused on three core constructs, namely perceived usefulness (PU), perceived ease of use (PEOU), and attitude towards use (AT). A total of 86 teachers participated in the study, of which 71 respondents were included in the final sample from four rural primary schools in the Seremban District. Data were collected using a TAM-based questionnaire and analysed using descriptive statistics and Pearson correlation analysis. The findings revealed that PU and PEOU were at a moderate level based on the mean scores. A strong and significant positive relationship was found between PU and AT (r = .83, p < .001), and similarly, between PEOU and AT (r = .83, p < .001). These findings suggest that although teachers’ perceptions of usefulness and ease of use remain moderate, both constructs are strongly associated with the formation of positive attitudes towards online teaching. Practically, the findings highlight the need for targeted professional development, continuous technical support, and improved digital infrastructure to enhance both the functional value and usability of educational technologies among rural educators.
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Adedoyin, O. B., & Soykan, E. (2023). Covid-19 pandemic and online learning: The challenges and opportunities. In Interactive Learning Environments (Vol. 31, Issue 2, pp. 863–875). Routledge. https://doi.org/10.1080/10494820.2020.1813180
Afzal, F., & Crawford, L. (2022). Student's perception of engagement in online project management education and its impact on performance: The mediating role of self-motivation. Project Leadership and Society, 3, 100057. https://doi.org/10.1016/j.plas.2022.100057
Ahiaku, P. K. A., Uleanya, C., & Muyambi, G. C. (2025). Rural schools and tech use for sustainability: The challenge of disconnection. Education and Information Technologies, 30, 12557–12571. https://doi.org/10.1007/s10639-024-13311-9
Akpen, C. N., Asaolu, S., Atobatele, S., Okagbue, H., & Sampson, S. (2024). Impact of online learning on student's performance and engagement: a systematic review. Discover Education, 3(1), 205. https://doi.org/10.1007/s44217-024-00253-0
Al Doghan, M. A., & Piaralal, S. K. (2024). Exploring the Impact of e-learning initiatives on students’ self-efficacy and e-learning experiences. Eurasian Journal of Educational Research, 2024(109), 158–176. https://doi.org/10.14689/ejer.2024.109.010
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/https://doi.org/10.1016/j.compedu.2018.06.008
Almahasees, Z., Mohsen, K., & Amin, M. O. (2021, May). Faculty’s and students’ perceptions of online learning during COVID-19. In Frontiers in education (Vol. 6, p. 638470). Frontiers Media SA. https://doi.org/10.3389/feduc.2021.638470
Alrumi, N. (2024). The impact of COVID-19 on medical education and training. British Journal of Hospital Medicine, 85(5), 1–7. https://doi.org/10.12968/hmed.2023.0462
Belew, A., Ketemaw, A., Sitotaw, G., Alganeh, H., Wogayehu, B., Ayenew, T., & Damtew, A. (2024). Acceptance of e-learning and associated factors among postgraduate medical and health science students at first generation universities in Amhara region, 2023: using modified technology acceptance model. BMC Medical Education, 24(1), 838. https://doi.org/10.1186/s12909-024-05834-z
Chang, G., Sukumaran, S., & Wang, J. (2023). Self-Regulated learning and academic achievement in blended learning in china. Environment-Behaviour Proceedings Journal, 8(26), 183–188. https://doi.org/10.21834/e-bpj.v8i26.5175
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
Chen Hsieh, J. S., Huang, Y. M., & Wu, W. C. V. (2017). Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior, 70, 178–190. https://doi.org/10.1016/j.chb.2016.12.066
Chen, L. H. (2023). Moving forward: International students’ perspectives of online learning experience during the pandemic. International Journal of Educational Research Open, 5, 100276. https://doi.org/10.1016/j.ijedro.2023.100276
Cheng, Z., Zhang, Z., Xu, Q., Maeda, Y., & Gu, P. (2025). A meta-analysis addressing the relationship between self-regulated learning strategies and academic performance in online higher education. Journal of Computing in Higher Education, 37(1), 195–224. https://doi.org/10.1007/s12528-023-09390-1
Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Laurence Erlbaum Associates.
Coman, C., Țîru, L. G., Meseșan-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective. Sustainability, 12(24), 10367. https://doi.org/10.3390/su122410367
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Dube, B. (2020). Rural online learning in the context of COVID-19 in south africa: Evoking an inclusive education approach. Multidisciplinary Journal of Educational Research, 10(2), 135–157. https://doi.org/10.4471/remie.2020.5607
Garland, R. (1991). The mid-point on a rating scale: Is it desirable. Marketing Bulletin, 2(1), 66-70. https://www.rangevoting.org/MB_V2_N3_Garland.pdf
Gherghel, C., Yasuda, S., & Kita, Y. (2023). Interaction during online classes fosters engagement with learning and self-directed study both in the first and second years of the COVID-19 pandemic. Computers & Education, 200, 104795. https://doi.org/10.1016/j.compedu.2023.104795
Gillett-Swan, J. (2017). The challenges of online learning: Supporting and engaging the isolated learner. Journal of learning design, 10(1), 20-30. https://doi.org/10.5204/jld.v9i3.293
Goh, T. T., Dai, X., & Yang, Y. (2023). Benchmarking ChatGPT for prototyping theories: Experimental studies using the technology acceptance model. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(4), 100153. https://doi.org/10.1016/j.tbench.2024.100153
Huang, T. (2023). Factors affecting students’ online courses learning behaviors. Education and Information Technologies, 28, 16485–16507. https://doi.org/10.1007/s10639-023-11882-7
Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196
Irni Hafizan, I. B., Muhammad Ihsan, R., Nik Muhammad Hanis, N. R., & Mohd Syaubari, O. (2024). Tahap penerimaan pelajar terhadap teknologi augmented reality dan hubungannya terhadap sikap dalam pembelajaran kimia organik. Evaluation Studies in Social Sciences, 5(1), 56–68. https://doi.org/10.37134/esss.vol5.1.4.2024
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning Journal, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092
Marzoli, I., Colantonio, A., Fazio, C., Giliberti, M., Scotti di Uccio, U., & Testa, I. (2021). Effects of emergency remote instruction during the COVID-19 pandemic on university physics students in Italy. Physical Review Physics Education Research, 17(2), 020130. https://doi.org/10.1103/PhysRevPhysEducRes.17.020130
Mohamad Khalid, R., Abdul Malik, S., Abd Aziz, N. A., Musa, M. H., & Abd Aziz, N. N. (2021). Rural area school teacher’s technology adoption: Lessons learned from the COVID-19 phenomenon. Environment-Behaviour Proceedings Journal, 6(18), 71–77. https://doi.org/10.21834/ebpj.v6i18.3059
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/https://doi.org/10.1016/j.chb.2014.07.044
Mustafa, F., Nguyen, H. T. M., & Gao, X. A. (2024). The challenges and solutions of technology integration in rural schools: A systematic literature review. International Journal of Educational Research, 126, 102380. https://doi.org/10.1016/j.ijer.2024.102380
Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students’ perception and preference for online education in India during COVID-19 pandemic. Social sciences & humanities open, 3(1), 100101. https://doi.org/10.1016/j.ssaho.2020.100101
Pallant, J. (2011). SPSS survival manual. A step by step guide to data analysis using SPSS (4th ed.). Allen & Unwin.
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of educational technology & society, 12(3), 150-162. https://doi.org/10.2307/jeductechsoci.12.3.150
Passey, D., Ntebutse, J. G., Ahmad, M. Y. A., Cochrane, J., Collin, S., Ganayem, A., Langran, E., Mulla, S., Rodrigo, M. M., Saito, T., Shonfeld, M., & Somasi, S. (2024). Populations digitally excluded from education: Issues, factors, contributions and actions for policy, practice and research in a post-pandemic era. Technology, Knowledge and Learning, 29, 1733–1750. https://doi.org/10.1007/s10758-024-09767-w
Peimani, N., & Kamalipour, H. (2021). Online education and the covid-19 outbreak: A case study of online teaching during lockdown. Education Sciences, 11(2), 1–16. https://doi.org/10.3390/educsci11020072
Peng, R., Abdul Razak, R., & Hajar Halili, S. (2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PloS one, 18(8), e0286112. https://doi.org/10.1371/journal.pone.0286112
Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103701
Saroia, A. I., & Gao, S. (2019). Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International, 56(5), 569–580. https://doi.org/10.1080/14703297.2018.1557068
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Tan, P. S. H., Seow, A. N., Choong, Y. O., Tan, C. H., Lam, S. Y., & Choong, C. K. (2024). University students’ perceived service quality and attitude towards hybrid learning: ease of use and usefulness as mediators. Journal of Applied Research in Higher Education, 16(5), 1500-1514. https://doi.org/10.1108/JARHE-03-2023-0113
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/https://doi.org/10.1016/j.compedu.2011.06.008
Thannimalai, T., Ponniah, K., Nawastheen, F. M., Jose, F. T., & Jaiseelan, S. (2022). Attitudes and acceptance of information and communication technology (ICT) among urban and rural teachers in teaching and facilitation. International Journal of Advanced and Applied Sciences, 9(7), 16–23. https://doi.org/10.21833/ijaas.2022.07.003
Tondeur, J., Scherer, R., Baran, E., Siddiq, F., Valtonen, T., & Sointu, E. (2019). Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education. British Journal of Educational Technology, 50(3), 1189–1209. https://doi.org/10.1111/bjet.12748
Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575. https://doi.org/10.1007/s11423-016-9481-2
Turkkila, M., & Lommi, H. (2020). Student participation in online content-related discussion and its relation to students’ background knowledge. Education Sciences, 10(4), 106. https://doi.org/10.3390/educsci10040106
Venkatesh, V., & Davis, F. D. (2000). A Theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://about.jstor.org/terms
Wijnen, F., Walma van der Molen, J., & Voogt, J. (2023). Primary school teachers’ attitudes toward technology use and stimulating higher-order thinking in students: a review of the literature. Journal of Research on Technology in Education, 55(4), 545–567. https://doi.org/10.1080/15391523.2021.1991864
Xu, B., Chen, N. S., & Chen, G. (2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & Education, 157, 103956. https://doi.org/10.1016/j.compedu.2020.103956
Yan, Y., Vyas, L., Wu, A. M., & Rawat, S. (2022). Effective online education under COVID-19: Perspectives from teachers and students. Journal of Public Affairs Education, 28(4), 422–439. https://doi.org/10.1080/15236803.2022.2110749
Zheng, M., Bender, D., & Lyon, C. (2021). Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study. BMC medical education, 21(1), 495. https://doi.org/10.1186/s12909-021-02909-z
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