Research Article

Vietnamese Teachers’ Acceptance to Use E-Assessment Tools in Teaching: An Empirical Study Using PLS-SEM

Thuy Thi Tang 1 * , Thuy Nga Nguyen 1 , Huong Thi Thu Tran 1
More Detail
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
OPEN ACCESS   2273 Views   1453 Downloads
Download Full Text (PDF)

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

  1. 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
  2. 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
  3. 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
  4. Appiah, M., & Van Tonder, F. (2018). E-assessment in higher education: A review. International Journal of Business Management & Economic Research, 9(6),1454-1460.
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  11. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic Press. https://doi.org/10.4324/9780203771587
  12. 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
  13. 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
  14. Davis, F. D. (2011). Foreword in technology acceptance in education: Research and issues. Sense Publishers.
  15. 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
  16. 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
  17. 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
  18. Garson, G. D. (2016). Partial least squares. Regression and structural equation models. Statistical Associates Publishers Publications.
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. MOET. (2021, July). Statistics. Ministry of Education and Training. https://moet.gov.vn/thong-ke/Pages/thong-ke.aspx
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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.
  41. 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
  42. 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
  43. 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.
  44. 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.
  45. 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
  46. Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. AsiaPacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779
  47. 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