The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Google classroom as a tool for active learning
Abstract
As the world is being developed with the new technologies, discovering and manipulating new ideas and concepts of online education are changing rapidly. In response to these changes, many states, institutions, and organizations have been working on strategic plans to implement online education. At the same time, misconceptions and myths related to the difficulty of teaching and learning online, technologies available to support online instruction, the support and compensation needed for high-quality instructors, and the needs of online students create challenges for such vision statements and planning documents. This paper provides analysis and evaluation of the effectiveness of Google Classroom’s active learning activities for data mining subject under the Decision Sciences program. Technology Acceptance Model (TAM) has been employed to measure the effectiveness of the learning activities. A total of 100 valid unduplicated responses from students who enrolled data mining subject were used in this study. The results indicated that majority of the students satisfy with the Google Classroom’s tool that were introduced in the class. Results of data analyzed showed that all ratios are above averages. In particular, comparative performance is good in the areas of ease of access, perceived usefulness, communication and interaction, instruction delivery and students’ satisfaction towards the Google Classroom’s active learning activities.
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“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 [62]
Citations
Cites
(1985) · Cited by 6,185
Internet Self-Efficacy and the Psychology of the Digital Divide
Eastin, M. S., & LaRose, R. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2000.tb00110.x · Cited by 1,168
Using the Facebook group as a learning management system: An exploratory study
Wang, Q., Woo, H. L., Quek, C. L., Yang, Y., & Liu, M. (2011) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2011.01195.x · Cited by 670
Joo, Y. J., Lim, K. Y., & Kim, E. K. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.02.008 · Cited by 405
George Saadé, R., Nebebe, F., & Tan, W. (2007) · Interdisciplinary Journal of e-Skills and Lifelong Learning · doi:10.28945/392 · Cited by 223
Zydney, J. M., deNoyelles, A., & Seo, K. K. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.07.009 · Cited by 126
Shea, P., & Bidjerano, T. (2011) · Interactive Learning Environments · doi:10.1080/10494820.2011.584320 · Cited by 80
Cited by
Sorted by citations
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
Metadata
- Authors
- Shaharanee, I. N. M., Jamil, J. M., & Rodzi, S. S. M.
- Affiliations
- Universiti Utara Malaysia (MY)
- Venue
- AIP Conference Proceedings
- Publisher
- Author(s)
- Published
- 2016
- Volume
- 1761
- Pages
- 020069
- ISSN
- 0094-243X (ISSN-L)
- OpenAlex
- W2511325517
- Cited by
- 137
- References
- 7
- Citations by year
- 2025: 2 · 2024: 14 · 2023: 28 · 2022: 28 · 2021: 38 · 2020: 17 · 2019: 9 · 2018: 1
- FWCI
- 26.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- AIP conference proceedings (publishedVersion)
- Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia) (submittedVersion)
- Topics
- Social Sciences › Education › Technology-Enhanced Education Studies
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- Google Classroom, active learning, online learning, data mining education, technology acceptance model, student satisfaction, perceived usefulness, ease of use, online learning tools, instructor support
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- CoChat. (2026). Citation check: Google classroom as a tool for active learning. https://app.cochat.ai/papers/10.1063/1.4960909
- The paper
- Shaharanee, I. N. M., Jamil, J. M., & Rodzi, S. S. M. (2016). Google classroom as a tool for active learning. AIP Conference Proceedings. https://doi.org/10.1063/1.4960909