Research Article
Vietnamese Teachers’ Acceptance to Use E-Assessment Tools in Teaching: An Empirical Study Using PLS-SEM
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1 VNU University of Education, Hanoi, VIETNAM* Corresponding Author
Contemporary Educational Technology, 14(3), July 2022, ep375, https://doi.org/10.30935/cedtech/12106
Published: 22 May 2022
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ABSTRACT
The purpose of this study is to examine factors that influence teachers’ intentions to use technology in assessments using the technology acceptance model (TAM) as a framework. An online survey was utilized to collect data, and 360 teachers participated in the survey. This study used partial least squares-structural equation modelling (PLS-SEM) to test the hypotheses to verify the effects of variables on teachers’ intention of e-assessment use. The model consists of four constructs: computer self-efficacy (CE), perceived ease of use (PEOU), perceived usefulness (PU), and frequent use of e-assessment tools (FoUAT). The findings revealed a significant influence path from CE to PEOU, FoUAT, and behavior intention. In addition, PEOU is a critical factor that positively impacts both PU and teachers’ behavior intentions. In contrast to our expectation, frequency of use was statistically insignificant and had no impact on teachers’ intention to use (IU) e-assessment tools. The total of these four variables corresponded to 71.4% of the variance of user intention. These results confirm that TAM is an effective model to explain teachers’ technology acceptance to use e-assessment tools for their teaching.
CITATION (APA)
Tang, T. T., Nguyen, T. N., & Tran, H. T. T. (2022). Vietnamese Teachers’ Acceptance to Use E-Assessment Tools in Teaching: An Empirical Study Using PLS-SEM. Contemporary Educational Technology, 14(3), ep375. https://doi.org/10.30935/cedtech/12106
REFERENCES
- Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
- 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/10.1016/j.compedu.2018.06.008
- Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64, 843-858. https://doi.org/10.1016/j.chb.2016.07.065
- Appiah, M., & Van Tonder, F. (2018). E-assessment in higher education: A review. International Journal of Business Management & Economic Research, 9(6),1454-1460.
- Ariff, M. S. M., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The effects of computer self-efficacy and technology acceptance model on behavioral intention in internet banking systems. Procedia-Social and Behavioral Sciences, 57, 448-452. https://doi.org/10.1016/j.sbspro.2012.09.1210
- Baydas, O., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930-945. https://doi.org/10.1080/10494820.2016.1232277
- Bhatt, S., & Shiva, A. (2020). Empirical examination of the adoption of Zoom software during COVID-19 pandemic: Zoom TAM. Journal of Content, Community & Communication, 12(6), 70-88. https://doi.org/10.31620/JCCC.06.%2020/08
- Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
- Cheng, K. H., & Tsai, C. C. (2011). An investigation of Taiwan University students’ perceptions of online academic help-seeking, and their web-based learning self-efficacy. The Internet and Higher Education, 14(3), 150-157. https://doi.org/10.1016/j.iheduc.2011.04.002
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
- Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic Press. https://doi.org/10.4324/9780203771587
- Conole, G., & Warburton, B. (2005). A review of computer-assisted assessment. ALT-J, Research in Learning Technology, 13(1), 17-31. https://doi.org/10.1080/0968776042000339772
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
- Davis, F. D. (2011). Foreword in technology acceptance in education: Research and issues. Sense Publishers.
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
- Davis, M. M., Spohrer, J. C., & Maglio, P. P. (2011). Guest editorial: How technology is changing the design and delivery of services. Operations Management Research, 4(1-2), 1-5. https://doi.org/10.1007/s12063-011-0046-6
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
- Garson, G. D. (2016). Partial least squares. Regression and structural equation models. Statistical Associates Publishers Publications.
- Granic, A., & Marangunic, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864
- Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE. https://doi.org/10.1007/978-3-030-80519-7
- Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. SAGE. https://doi.org/10.15358/9783800653614
- Hong, X., Zhang, M., & Liu, Q. (2021). Preschool teachers’ technology acceptance during the COVID-19: An adapted technology acceptance model. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.691492
- Huang, F., Teo, T., & Scherer, R. (2020). Investigating the antecedents of university students’ perceived ease of using the Internet for learning. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2019.1710540
- Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889. https://doi.org/10.4314/jfas.v9i4s.50
- Kanwal, F., & Rehman, M. (2017). Factors affecting e-learning adoption in developing countries-empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968-10978. https://doi.org/10.1109/ACCESS.2021.2714379
- Kundu, A., & Bej, T. (2020). Experiencing e-assessment during COVID-19: An analysis of Indian students’ perception. Higher Education Evaluation and Development, 15(2), 114-134. https://doi.org/10.1108/heed-03-2021-0032
- Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788
- Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
- Marangunic, N., & Granic, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81-95. https://doi.org/10.1007/s10209-014-0348-1
- MOET. (2021, July). Statistics. Ministry of Education and Training. https://moet.gov.vn/thong-ke/Pages/thong-ke.aspx
- Mukminin, A., Habibi, A., Muhaimin, M., & Prasojo, L. D. (2020). Exploring the drivers predicting behavioral intention to use m-learning management system: Partial least square structural equation model. IEEE Access, 8, 181356-181365. https://doi.org/10.1109/ACCESS.2020.3028474
- Ngabiyanto, Nurkhin, A., Mukhibad, H., & Harsono. (2021). E-learning evaluation using general extended technology acceptance model approach at schools in COVID-19 pandemic. European Journal of Educational Research, 10(3), 1171-1180. https://doi.org/10.1088/1742-6596/1783/1/012123
- Purnomo, S. H., & Lee, Y. H. (2013). E-learning adoption in the banking workplace in Indonesia: An empirical study. Information Development, 29(2), 138-153. https://doi.org/10.1177/0266666912448258
- Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732
- Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468. https://doi.org/10.3390/ijerph17186468
- Sanchez-Prieto, J. C., Olmos-Miguelanez, S., & Garcia-Penalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519-528. https://doi.org/10.1016/j.chb.2015.07.002
- 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 & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
- Stockless, A. (2018). Acceptance of learning management system: The case of secondary school teachers. Education and Information Technologies, 23(3), 1101-1121. https://doi.org/10.1007/s10639-017-9654-6
- Teo, T., Ruangrit, N., Khlaisang, J., Thammetar, T., & Sunphakitjumnong, K. (2014). Exploring e-learning acceptance among university students in Thailand: A national survey. Journal of Educational Computing Research, 50(4). https://doi.org/10.2190/EC.50.4.c
- Teo, T., Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of pre-service teachers’ technology acceptance in Turkey: A structural equation modeling approach. Asia-Pacific Education Researcher, 21(1), 191-202.
- Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540
- Weerasinghe, S., & Hindagolla, M. (2017). Technology acceptance model in the domains of LIS and education: A review of selected literature. Library Philosophy & Practice, 1582, 1-26.
- Wirtz, B. W., & Göttel, V. (2016). Technology acceptance in social media: Review, synthesis and directions for future empirical research. Journal of Electronic Commerce Research, 17(2), 97-115.
- Yalcin, M. E., & Kutlu, B. (2019). Examination of students’ acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Technology, 50(5), 2414-2432. https://doi.org/10.1111/bjet.12798
- Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779
- Zainab, B., Bhatti, M. A., & Alshagawi, M. (2017). Factors affecting e-training adoption: An examination of perceived cost, computer self-efficacy and the technology acceptance model. Behavior & Information Technology, 36(12), 1261-1273. https://doi.org/10.1080/0144929X.2017.1380703