Students' Adoption of Google Classroom Investigated by Technology Acceptance Model


  • Rajib Lochan Das Associate Professor and Additional Director, Department of Quantitative Sciences, International University of Business Agriculture and Technology, Bangladesh.



COVID-19 Pandemic, Google Classroom, Online Class, Learning Management System, Teaching-Learning, Technology Acceptance Model


Educational institutions in Bangladesh had to stop face-to-face educational activities during the COVID-19 pandemic. Institutions accepted online classes as the only alternative for conducting educational activities. Teachers and students transitioned from face-to-face to online medium by using a Learning Management System (LMS). Google Classroom has evolved as an LMS during this period. This study uses a quantitative research methodology to focus on students' adoption of Google Classroom in higher education using the modified version of the Technology Acceptance Model (TAM). In particular, this research aims to identify the factors affecting university students' behavioural intention towards LMS. The revised LMS-TAM model was used with prediction factors like perceived usefulness, perceived ease of use, behavioural intent, enjoyment, subjective norm, satisfaction, and interactivity and control. A questionnaire was developed with experts' opinions and distributed online to the respondents. 185 university students from different disciplines gave responses to the questionnaire. A set of recommendations are formulated based on the statistical results. The researchers are hopeful that higher education institutions, teachers, technical support staff, instructional designers and policymakers will benefit from this study.


Download data is not yet available.




How to Cite

Das, R. L. (2023). Students’ Adoption of Google Classroom Investigated by Technology Acceptance Model. MIER Journal of Educational Studies Trends and Practices, 13(1), 98–113.





Ajzen, I. (1991). The Theory of planned behavior. Organizational Behavior and Human Processes, 50(2), 179-211.

Allen, I., & Seaman, J. (2009). Learning on demand: Online education in the United States. Babson Survey Research Group. ERIC.

Asian Development Bank. (2017). Innovative strategies for accelerated human resource development in South Asia: Information and communication technology for education-Special focus on Bangladesh, Nepal, and Sri Lanka.

Bandura, A. (1986). Social foundations of thought and action: A Social cognitive theory. Prentice Hall.

Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012), Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855.

Borboa, D., Joseph, M., Spake, D., & Yazdanparast A. (2014). Perceptions and use of learning management system tools and other technologies in higher education: A preliminary analysis. Journal of Learning in Higher Education, 10(2), 17-23.

Dahiya, M., & Malik, N. (2021), A survey of teachers' and students' perspective on e-learning during Covid-19 in Delhi. MIER Journal of Educational Studies Trends & Practices, 11(2), 200 – 222.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

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.

Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A Ten-year update. Journal of Management Information Systems, 19(4), 9-30.

Findik-Coskuncay, D., Alkis, N., & Ozkan-Yildirim, S. (2018). A Structural Model for Students' Adoption of Learning Management Systems: An Empirical Investigation in the Higher Education Context. Educational Technology & Society, 21(2), 13–27.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.

Goh, C.F., Hii, P.K., Tan, O.K., & Rasli, A. (2020). Why do university teachers use e-learning systems? International Review of Research in Open and Distributed Learning, 21(2), 136-155.

Google for Education. (2021). Google Classroom: Where teaching and learning come together. Google LLC, CA.

Green, S. M., Weaver, M., Voegeli, D., Fitzsimmons, D., Knowles, J., Harrison, M., & Shephard, K. (2006). The development and evaluation of the use of a virtual learning environment (blackboard 5) to support the learning of pre-qualifying nursing students undertaking a human anatomy and physiology module. Nurse Education Today, 26(5), 388- 395.

Heggart, K. R., & Yoo, J. (2018). Getting the most from google classroom: A pedagogical framework for tertiary educators. Australian Journal of Teacher Education, 43(3).

Jasim, M. M. (2020, April 4). Private university students taking online classes. The Business Standard.

Lee, J., & Cha, K. (2021). A study on google classroom as a tool for the development of the learning model of college English. International Journal of Contents,17(2).

Lee, Y.-C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423-1438.

Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners' acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329.

McMillan, J. H., & Schumacher, S. (1993). Research in education: A conceptual understanding. Harper Collins.

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162.

Ra, S., Chin, B., & Lim, C. P. (2016). A holistic approach towards information and communication technology for addressing education challenges in Asia and the Pacific. Educational Media International, 53 (2), 69–84.

Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696.

Saade, R., & Bahli, B. (2005). The Impact of cognitive absorption on perceived usefulness and perceived ease of use in online learning: An Extension of the technology acceptance model. Information & Management, 42(2), 317-327.

Sakib, A. N. (2020, 16th March). COVID-19: Bangladesh Shuts All Educational Institutions. Anadolu Agency.

Schacter, J. (1999). The Impact of Education Technology on Student Achievement: What the Most Current Research Has to Say. Santa Monica, California: The Milken Family Foundation.

Sorebo, O., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The Role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177-1187.

Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.

Traxler, J. (2018). Distance Learning-Predictions and Possibilities. Education Science, 8(35).

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2),186-204.

Wiersma, W. (1995). Research methods in education: An introduction (6th ed.). Simon & Schuster Company.

World Health Organization. (2020, March 11). WHO Director-General's Opening Remarks at the Media Briefing on Covid-19. WHO.

Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449.