Students Acceptance of Google Classroom: An Exploratory Study using PLS-SEM Approach
Open accessAbstract
Google classroom can work in unidirectional process as it can serve the teachers’ strategies and styles on one hand and students’ perception, understanding, and effective participation in different classroom skills on the other hand. The ac-ceptance of Google classroom is affected by different factors. Some of them are still not clearly specified and discussed in previous research; therefore, they need further investigation. Based on the previous assumption, this study is an attempt to examine the factors that affect the students’ acceptance of Google classroom at Al Buraimi University College (BUC) in Oman. The Technology Acceptance Model (TAM) was adopted to formulate the hypotheses of the current study. The data was collected through an online questionnaire with 337 respondents. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. The results of the study prove that both the perceived ease of use (PEOU) and perceived usefulness (PU) positively influence the behavioral intention, which in turn influence the actual usage of Google classrooms. This study helps the decision makers of the higher educational institutions to have a better understanding of the effectiveness of us-ing Google classroom by their students. It is assumed that it helps in measuring the level of students’ acceptance to the previously mentioned technology.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Google Classroom improves class dynamics and helps with learner participation.
“Research shows that it improves class dynamics and helps with learner participation [2, 29, 62, 63, 69].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · see citing works
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921 · see citing works
Investigating attitudes towards the use of mobile learning in higher education
Al‐Emran, M., Elsherif, H. M., & Shaalan, K. F. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.033 · Cited by 602 · see citing works
Self-citationThe Application of Google Classroom as a Tool for Teaching and Learning
Shaharanee, I. N. M., Jamil, J. M., & Rodzi, S. S. M. (2016) · Journal of Telecommunication Electronic and Computer Engineering (JTEC) · Cited by 219 · see citing works
Jakkaew, P., & Hemrungrote, S. (2017) · doi:10.1109/icdamt.2017.7904962 · Cited by 103 · see citing works
Students' Attitudes Towards the Use of Mobile Technologies in e-Evaluation
Al‐Emran, M., & Salloum, S. A. (2017) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v11i5.6879 · Cited by 95 · see citing works
Self-citationStudents' Perceptions of E-learning at the University of Jordan
Almarabeh, T. (2014) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v9i3.3347 · Cited by 91 · see citing works
The Impact of Google Apps at Work: Higher Educational Perspective
Al‐Emran, M., & Malik, S. I. (2016) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v10i4.6181 · Cited by 48 · see citing works
Self-citationAcademics' Awareness Towards Mobile Learning in Oman
Al‐Emran, M. (2017) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/060105 · Cited by 36 · see citing works
Self-citationHemrungrote, S., Jakkaew, P., & Assawaboonmee, S. (2017) · doi:10.1109/icdamt.2017.7904961 · Cited by 35 · see citing works
The effects of Google Classroom on teaching social studies for students with learning disabilities
DiCicco, K. M. (2016) · Rowan Digitals Works (Rowan University) · Cited by 34 · see citing works
Learning Engagement in Virtual Environment
Latif, S. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016911289 · Cited by 29 · see citing works
"E-podium Technology: A medium of managing Knowledge at Al Buraimi University College via Mlearning"
Al‐Emran, M., & Shaalan, K. F. (2015) · Electronic workshops in computing · doi:10.14236/ewic/bcsme2014.14 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- J. E. Gallagher, K. A. Dobrosielski-vergona, R. G. Wingard, and T. M. Williams, "Web- based vs . Traditional Classroom Instruction in Gerontology": A Pilot Study," vol. 79, no. 3, pp. 1-10, 2005.
- S. Iftakhar, "Google classroom: what works and how?," J. Educ. Soc. Sci., vol. 3, no. Feb, pp. 12-18, 2016.
- I. Fishbein, M. & Ajzen, "Belief, attitude, attitude, intention and behavior: An introduction to theory of research," Reading, MA Addison-Wesley Addison-Wesley, p. 578, 1975.
Cited by
Sorted by citations
Albayati, H., Kim, S. K., & Rho, J. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101320 · Cited by 464 · see citing works
Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425 · see citing works
Li, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157 · see citing works
Salloum, S. A., & Shaalan, K. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-99010-1_43 · Cited by 128 · see citing works
- Authors
- Al-Maroof, R. A. S., & Al-Emran, M.
- Venue
- International Journal of Emerging Technologies in Learning (iJET)
- Published
- 2018-05-29
- Volume
- 13
- Issue
- 06
- Pages
- 112
- ISSN
- 1863-0383 (ISSN-L)
- OpenAlex
- W2806369404
- Cited by
- 424
- References
- 14
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 33 · 2023: 64 · 2022: 90 · 2021: 107 · 2020: 63 · 2019: 26 · 2018: 6
- FWCI
- 163.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Emerging Technologies in Learning (iJET) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Technology-Enhanced Education Studies
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Online and Blended Learning
- Keywords
- Google Classroom, student acceptance, technology acceptance, technology acceptance model, perceived ease of use, perceived usefulness, behavioral intention, higher education, online learning platforms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review