Extended Technology Acceptance Model to Examine the Use of Google Forms – based Lesson Playlist in Online Distance Learning





digital learning technology, extended technology acceptance model, COVID-19, online distance learning, Philippine Senior High School students, PLS-SEM


Shifting to online distance learning due to the COVID-19 pandemic challenged educators' roles as instructional materials designers. This study aimed to examine the students' acceptance of the teacher-designed e-learning tool called Google Forms-based Lesson Playlist (GF-LP) in a home-based online distance learning environment. This quantitative research analyzed 570 responses from Grades 11 and 12 students at a private school in the Philippines using the partial least squares-structural equation modeling. Results showed that perceived self-efficacy and system quality strongly affect the users' perceived ease of use while perceived ease of use highly influenced the students' perceived usefulness of GF-LP. Facilitating conditions do not affect the users' attitudes towards using the e-learning tool, which confirmed the effective utilization of GF-LP in online distance learning. The relationships between the original constructs of the Technology Acceptance Model (TAM) were also presented. This study recommends the use of GF-LP or its features for remote learning.


Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students’ perspectives. Journal of Pedagogical Sociology and Psychology, 2(1), 45-51. https://doi.org/10.33902/JPSP.

Almaiah, M. A., Alamri, M. M., & Al-rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206

Ancho, I. (2020). Graduate education during COVID-19 pandemic: Inputs to policy formulation in the new normal. Recoletos Multidisciplinary Research Journal, 8(2), 87-105. https://doi.org/10.32871/rmrj2008.02.07

Asiimwe, E. N., & Grönlund, Å. (2015). MLCMS actual use, perceived use, and experiences of use. International Journal of Education and Development Using Information and Communication Technology, 11(1), 101–121. https://files.eric.ed.gov/fulltext/EJ1061487.pdf

Binyamin, S. S., Rutter, M. J., & Smith, S. (2019). Extending the Technology Acceptance Model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning, 14(3), 4–21. https://doi.org/10.3991/ijet.v14i03.9732

Bohol, D. O., & Prudente, M. S. (2020, January 10-12). Using lesson playlist through schoology-based Flipping The Classroom (FTC) approach in enhancing STEM students’ performance in General Biology 1 [Conference session]. The 11th International Conference on E-Education, E-Business, E-Management, and E-Learning, Osaka, Japan. https://doi.org/10.1145/3377571.3377588

Brown, M., Dehoney, J., & Millichap, N. (2015). The next generation digital learning environment: A report on research [ELI Paper]. https://library.educause.edu/resources/2015/4/the-next-generation-digital-learning-environment-a-report-on-research

Chen, T., Peng, L., Jing, B., Wu, C., Yang, J., & Cong, G. (2020). The impact of the COVID-19 pandemic on user experience with online education platforms in China. Sustainability, 12(18), 7329. https://doi.org/10.3390/SU12187329

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

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210–232. https://www.researchgate.net/publication/281842180_Expanding_The_Technology_Acceptance_Model_TAM_to_Examine_Faculty_Use_of_Learning_Management_Systems_LMSs_In_Higher_Education_Institutions

Fearnley, M.R., & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education, 8(2), 89–106. https://doi.org/10.22492/ije.8.2.05

Fei, V. L., & Hung, D. (2016). Teachers as learning designers: What technology has to do with learning. A view from Singapore. Educational Technology, 56(4), 26–29. https://www.researchgate.net/publication/306080401_Teachers_as_Learning_Designers_What_Technology_has_to_do_with_Learning_A_View_from_Singapore#:~:text=It%20proposes%20that%20as%20a,the%20mastery%20of%20subject%20knowledge.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.1177/002224378101800104

Granić, A., & Marangunić, 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

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the Technology Acceptance Model for use of e-learning systems by digital learners. IEEE Access, 6, 73395–73404. https://doi.org/10.1109/ACCESS.2018.2881384

Janadari, M. P. N., Ramalu, S. S., & Wei, C. C. (2018). Measurement of organizational citizenship behaviour; reliability and validity in Sri Lankan context. Kelaniya Journal of Human Resource Management, 13(1), 1-9. http://doi.org/10.4038/kjhrm.v13i1.45

Kashada, A., Li, H., & Koshadah, O. (2018). Analysis approach to identify factors influence digital learning technology adoption and utilization in developing countries. International Journal of Emerging Technologies in Learning, 13(2), 48–59. https://doi.org/10.3991/ijet.v13i02.7399

Kock, N. (2020). WarpPLS 7.0 user manual: Version 7.0. https://www.scriptwarp.com/warppls/UserManual_v_7_0.pdf

König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education, 43(4), 608-622. https://doi.org/10.1080/02619768.2020.1809650

Lin, H. M., Lee, M. H., Liang, J. C., Chang, H. Y., Huang, P., & Tsai, C. C. (2020). A review of using partial least square structural equation modeling in e‐learning research. British Journal of Educational Technology, 51(4), 1354-1372. https://doi.org/10.1111/bjet.12890

Mercader, C., & Gairín, J. (2020). University teachers’ perception of barriers to the use of digital technologies: The importance of the academic discipline. International Journal of Educational Technology in Higher Education, 17(4). https://doi.org/10.1186/s41239-020-0182-x

Mothibi, G. (2015). A meta-analysis of the relationship between e-learning and students’ academic achievement in higher education. Journal of Educational and Practice, 6(9), 6–10. https://www.iiste.org/Journals/index.php/JEP/article/view/21025

Nikou, S. A., & Economides, A. A. (2019). Factors that influence behavioral intention to use mobile-based assessment: A STEM teachers’ perspective. British Journal of Educational Technology, 50(2), 587–600. https://doi.org/10.1111/bjet.12609

Ozerbas, M. A., & Erdogan, B. H. (2016). The effect of the digital classroom on academic success and online technologies self-efficacy. Educational Technology and Society, 19(4), 203–212. https://eric.ed.gov/?id=EJ1115665#:~:text=This%20study%20aimed%20to%20observe,efficacy%20of%207th%20grade%20students.&text=However%2C%20it%20has%20been%20shown,'%20online%20technologies%20self%2Defficacy.

Panda, S., & Mishra, S. (2007). E-Learning in a Mega Open University: Faculty attitude, barriers, and motivators. Educational Media International, 44(4), 323–338. https://doi.org/10.1080/09523980701680854

Putra, I. D. G. R. D. (2019). The evolution of Technology Acceptance Model (Tam) and recent progress on technology acceptance research in Elt: State of the art article. Yavana Bhasha : Journal of English Language Education, 1(2), 25–37. https://doi.org/10.25078/yb.v1i2.724

Rajab, M. H., Gazal, A. M., & Alkattan, K. (2020). Challenges to online medical education during the COVID-19 pandemic. Cureus, 12(7), e8966. https://doi.org/10.7759/cureus.8966

Salloum, S. A. (2018). Investigating students’ acceptance of E-learning system in higher educational environments in the UAE: Applying the extended Technology Acceptance Model (TAM) [Master’s thesis, The British University in Dubai]. The British in Dubai Digital Repository. https://bspace.buid.ac.ae/handle/1234/1150

Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467

Scherer, R., Siddiq, F., & Tondeur, J. (2020). All the same or different? Revisiting measures of teachers' technology acceptance. Computers & Education, 143, 103656. https://doi.org/10.1016/j.compedu.2019.103656

Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035–2057. https://doi.org/10.1007/s10639-018-09859-y

Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, demographic characteristics and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72-89. https://doi.org/10.5539/hes.v6n3p72

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184. https://doi.org/10.2190/EC.51.2.b

Teeroovengadum, V., Heeraman, N., & Jugurnath, B. (2017). Examining the antecedents of ICT adoption in education using an Extended Technology Acceptance Model (TAM). International Journal of Education and Development Using Information and Communication Technology, 13(3), 4–23. https://files.eric.ed.gov/fulltext/EJ1166522.pdf

Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. https://doi.org/10.1016/j.csda.2004.03.005

Teo, T., & Huang, F. (2019). Investigating the influence of individually espoused cultural values on teachers’ intentions to use educational technologies in Chinese universities. Interactive Learning Environments, 27(5–6), 813–829. https://doi.org/10.1080/10494820.2018.1489856

Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940

Toquero, C. M. (2020). Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context. Pedagogical Research, 5(4). https://doi.org/10.29333/pr/7947

Zanjani, N., Edwards, S. L., Nykvist, S., & Geva, S. (2017). The important elements of LMS design that affect user engagement with e-learning tools within LMSs in the higher education sector. Australasian Journal of Educational Technology, 33(1), 19–31. https://doi.org/10.14742/ajet.2938




How to Cite

Ferran, F. M. (2021). Extended Technology Acceptance Model to Examine the Use of Google Forms – based Lesson Playlist in Online Distance Learning. Recoletos Multidisciplinary Research Journal, 9(1), 147–161. https://doi.org/10.32871/rmrj2109.01.13