Email this article Google Classroom Adoption as Learning Management System in Senior High School Using Technology Acceptance Model
Abstract
Abstract: Google Classroom Adoption as Learning Management System in Senior High School Using Technology Acceptance Model. Objectives: The purpose of this study was to determine the acceptance of Google Classroom as a learning management system for public high school at one state university in Laguna, Philippines, using the Technology Acceptance Model (TAM). It further determined the effect of online learning self-efficacy on the original TAM-related factors. Methods: Using a quantitative through survey method, the data were collected using a web-based program from 742 students and were analyzed using structural equation modelling. The content-validated research instruments was adapted from previous studies (Al-Maroof and Al-Meran, 2018, Fathema et al, 2015, Zimmerman and Kulikowich, 2016). Findings: Findings revealed that all hypotheses were supported. Both perceived ease of use (PEOU) and perceived usefulness (PU) positively influence the attitude towards use (ATU), according to the studys findings. The PU and ATU can likewise significantly predict behavioral intention to use (BIU), which in turn explains the actual usage (AU) of Google classrooms. Furthermore, Online learning self-efficacy (OLSE) demonstrates a positive impact on both PU and PEOU. Conclusion: This study concludes that the students found Google Classroom as an effective tool for teaching and learning, as evidenced by a positive perspective and strong intent to continue using the platform. The salient implications for implementing LMS-based courses and future lines of research were also discussed.
Full Text:
PDFReferences
Al-Maroof, R. A. S. & Al-Emran, M. (2018). Students’ acceptance of Google Classroom: An exploratory study using PLS-SEM Approach. International Journal of Emerging Technologies in Learning, 13(6), 112-123. https://doi.org/10.3991/ijet.v13i06.8275
Andal, E. Z., Panergayo, A. A. E. & Almanza, M. R. G. (2020). Exploring the online learning self-efficacy of teacher education students in Laguna State Polytechnic University: Basis for transition to flexible learning system. Universal Journal of Educational Research, 8(12), 6598-6608. https://doi.org/10.13189/ujer.2020.081224
Awang, Z. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation Modelling. Bandar Baru Bangi, MPWS Rich Resources.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Browne, M. W. & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K. A. & Long, J. S. (Eds.) Testing Structural Equation Models. 136–162. Beverly Hills, CA: Sage.
Dash, S. (2019). Google classroom as a learning management system to teach biochemistry in a medical school. Biochemistry and Molecular Biology Education, 47(4), 404–407. https://doi.org/10.1002/bmb.21246
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64, 95–104. https://doi.org/10.1016/j.sbspro.2012.11.012
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.
Fearnley, M. R., & Amora, J. (2020). Learning Management System Adoption in Higher Education Using the Extended Technology Acceptance Model. IAFOR Journal of Education: Technology in Education, 8(2), 89–106. https://doi.org/10.22492/ije.8.2.05
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/10.1177/002224378101800104
Garcia, M. B. (2017). E-Learning Technology Adoption in the Philippines: An Investigation of Factors Affecting Filipino College Students’ Acceptance of Learning Management Systems. International Journal of E-learning and Educational Technologies in the Digital Media, 3(3), 118–130. https://doi.org/10.17781/p002374
George, D. & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.
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
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Holden, H. M., & Rada, R. (2011). Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers’ Technology Acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576
Holden, R. J., & Karsh, B. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002
Hsu, L., Tourism, K., S. A., & Chen, C. (2018). Examining Effectiveness of m-Learning in Hospitality Education with Revised Technology Acceptance Model. International Journal of Information and Education Technology, 8(8), 565–569. https://doi.org/10.18178/ijiet.2018.8.8.1100
Ifenthaler, D. (2012) Learning Management System. In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_187
Jöreskog, K. G., & Sörbom, D. (1993). Lisrel 8: Structural Equation Modeling with the Simplis Command Language. http://ci.nii.ac.jp/ncid/BA23533377
Marsh, H. W., Hau, K.-T., & Wen, Z (2004). In Search of the Golden Rules: Comment on the HypothesisTesting Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler’s (1999) Findings. Structured Equation Modeling 11(3), 320-341.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Panergayo, A. A. E., & Aliazas, J. V. C. (2021). Students’ Behavioral Intention to Use Learning Management System: The Mediating Role of Perceived Usefulness and Ease of Use. International Journal of Information and Education Technology, 11(11), 538–545. https://doi.org/10.18178/ijiet.2021.11.11.1562
Panergayo, A. A. E., & Mansujeto, K. A. (2022). Assessment of self-efficacy in online learning of teacher education students in one state university in the Philippines. International Journal of Computing Sciences Research, 6, 680–693. https://doi.org/10.25147/ijcsr.2017.001.1.65
Rhode, J., Richter, S., Gowen, P., Miller, T., & Wills, C. (2017). Understanding faculty use of the learning management system. Online Learning, 21(3), 68–86.
https://doi:10.24059/olj.v%vi%i.1217
Shaharanee, I. N. M., Jamil, J. M., & Rodzi, S. S. M. (2016). The Application of Google Classroom as a Tool for Teaching and Learning. Journal of Telecommunication, Electronic and Computer Engineering, 8(10), 5–8. http://repo.uum.edu.my/20521/
Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 36(10), 1053–1066. https://doi.org/10.1080/0144929x.2017.1340973
Shroff, R. H., Deneen, C. C. & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioral intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600-618. http://www.ascilite.org.au/ajet/ajet27/shroff.html
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
Surendran, P. (2012). Technology Acceptance Model: A Survey of Literature. International Journal of Business and Social Research, 2(4), 175–178. https://doi.org/10.18533/ijbsr.v2i4.161
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Turnbull D., Chugh R., Luck J. (2019) Learning Management Systems: An Overview. In: Tatnall A. (eds) Encyclopedia of Education and Information Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-60013-0_248-1
Van Griethuijsen, R. a. L. F., Van Eijck, M., Haste, H., Brok, P. D., Skinner, N., Mansour, N., Gencer, A. S., & BouJaoude, S. (2015a). Global Patterns in Students’ Views of Science and Interest in Science. Research in Science Education, 45(4), 581–603. https://doi.org/10.1007/s11165-014-9438-6
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. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. Management Information Systems Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Wedari, L. K., Fatihah, A. N., & Rusmanto, T. (2022a). Zoom Application Acceptance in Online Learning: An Analysis with the Technology Acceptance Model. International Journal of Information and Education Technology, 12(9), 821–830. https://doi.org/10.18178/ijiet.2022.12.9.1690
Wu, Y. (2006). University Students’ Internet attitudes and internet self-efficacy: a study at three universities in Taiwan. Cyber Psychology & Behavior, 9(4).
Wu, Y., & Tsai, C. C. (2006). University Students’ Internet Attitudes and Internet Self-Efficacy: A Study at Three Universities in Taiwan. Cyberpsychology & Behavior, 9(4), 441–450. https://doi.org/10.1089/cpb.2006.9.441
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Jurnal Pendidikan Progresif
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats
The copyright is reserved to The Jurnal Pendidikan Progresif that is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.