The Digital Footprint of University Learning: Academic Behavior in Google Classroom
DOI:
https://doi.org/10.55040/xjwyfg59Keywords:
learning, compliance, performance, engagement, assessmentAbstract
The widespread use of Learning Management Systems (LMS) generates structured academic data in higher education; however, there is limited descriptive evidence at the course level in Google Classroom as an autonomous analytical unit. The objective of this study was to examine academic behavior recorded in Google Classroom during the semester-long development of a university course. The study was quantitative, non-experimental, descriptive, and single-case in design; digital records from 24 students were analyzed, including 336 academic events related to submissions, statuses, and grades, using descriptive statistics. Results showed a predominance of compliance, with 85.7% of on-time submissions, 5.4% late submissions, and 8.9% non-submissions. High grades (18–20/20) were associated with completed tasks, while scores of 0/20 corresponded exclusively to non-submitted activities. Google Classroom’s internal metrics enabled the characterization of academic behavior at the course level; non-compliance explains performance variation, supporting descriptive analysis as an autonomous analytical unit.
References
Abdullah Saimi, W. M. S. B., & Mohamad, M. (2022). The Effectiveness of Google Classroom as a Virtual Learning Environment (VLE) for School Teachers: Literature Review. International Journal of Linguistics, Literature and Translation, 5(3), 172-175. https://doi.org/10.32996/ijllt.2022.5.3.22
Almusharraf, N., & Khahro, S. (2020). Students Satisfaction with Online Learning Experiences during the COVID-19 Pandemic. International Journal of Emerging Technologies in Learning (iJET), 15(21), 246. https://doi.org/10.3991/ijet.v15i21.15647
Alzahrani, N., Meccawy, M., Samra, H., & El-Sabagh, H. A. (2025). Identifying Weekly Student Engagement Patterns in E-Learning via K-Means Clustering and Label-Based Validation. Electronics, 14(15), 3018. https://doi.org/10.3390/electronics14153018
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 50. https://doi.org/10.1186/s41239-021-00282-x
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
Cerezo, R., Bogarín, A., Esteban, M., & Romero, C. (2020). Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education, 32(1), 74-88. https://doi.org/10.1007/s12528-019-09225-y
Creswell, J., & Creswell, D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Dai, W., Lin, J., Jin, F. J.-Y., Tsai, Y.-S., Srivastava, N., Le Bodic, P., Gašević, D., & Chen, G. (2025). Learning Analytics for Early Identification of At-Risk Students and Feedback Intervention. Journal of Learning Analytics, 12(3), 102-125. https://doi.org/10.18608/jla.2025.8735
Dawson, S., Joksimovic, S., Poquet, O., & Siemens, G. (2019). Increasing the Impact of Learning Analytics. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 446-455. https://doi.org/10.1145/3303772.3303784
De Brito Lima, F., Lautert, S. L., & Gomes, A. S. (2021). Contrasting levels of student engagement in blended and non-blended learning scenarios. Computers & Education, 172, 104241. https://doi.org/10.1016/j.compedu.2021.104241
Du, J., Liu, L., & Zhao, S. (2025). Empowering Students in Online Learning Environments Through a Self-Regulated Learning–Enhanced Learning Management System. Behavioral Sciences, 15(8), 1041. https://doi.org/10.3390/bs15081041
Ellis, R. A., & Bliuc, A.-M. (2019). Exploring new elements of the student approaches to learning framework: The role of online learning technologies in student learning. Active Learning in Higher Education, 20(1), 11-24. https://doi.org/10.1177/1469787417721384
Fernández, M. N., & Alder, I. (2023). Aulas de montaña: Escenarios pedagógicos. MENTOR revista de investigación educativa y deportiva, 2(6), 1068-1086. https://doi.org/10.56200/mried.v2i6.6461
Flores Buitrón, D. M., Fueres Lita, E. R., González Malput, A. N., & Macas Rosario, A. B. (2024). Explorando el impacto positivo del juego de roles en la segunda infancia. MENTOR revista de investigación educativa y deportiva, 3(8), 419-436. https://doi.org/10.56200/mried.v3i8.7855
Gikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers & Education, 57(4), 2333-2351. https://doi.org/10.1016/j.compedu.2011.06.004
Heggart, K., Yoo, J., & Australian Catholic University. (2018). Getting the Most from Google Classroom: A Pedagogical Framework for Tertiary Educators. Australian Journal of Teacher Education, 43(3), 140-153. https://doi.org/10.14221/ajte.2018v43n3.9
Ifenthaler, D. (2022). A systems perspective on data and analytics for distance education. Distance Education, 43(2), 333-341. https://doi.org/10.1080/01587919.2022.2064828
Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923-938. https://doi.org/10.1007/s11423-016-9477-y
Khalil, M., Slade, S., & Prinsloo, P. (2024). Learning analytics in support of inclusiveness and disabled students: A systematic review. Journal of Computing in Higher Education, 36(1), 202-219. https://doi.org/10.1007/s12528-023-09363-4
Lampropoulos, G., & Evangelidis, G. (2025). Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis. Applied Sciences, 15(2), 971. https://doi.org/10.3390/app15020971
Li, Q., Jung, Y., & Wise, A. F. (2026). How instructors use learning analytics: The pivotal role of pedagogy. Journal of Computing in Higher Education, 38(1), 227-255. https://doi.org/10.1007/s12528-025-09432-w
Liu, Y., Fan, S., Xu, S., Sajjanhar, A., Yeom, S., & Wei, Y. (2022). Predicting Student Performance Using Clickstream Data and Machine Learning. Education Sciences, 13(1), 17. https://doi.org/10.3390/educsci13010017
Matcha, W., Gašević, D., Ahmad Uzir, N., Jovanović, J., Pardo, A., Lim, L., Maldonado-Mahauad, J., Gentili, S., Pérez-Sanagustín, M., & Tsai, Y.-S. (2020). Analytics of Learning Strategies: Role of Course Design and Delivery Modality. Journal of Learning Analytics, 7(2), 45-71. https://doi.org/10.18608/jla.2020.72.3
Posso Pacheco, R. J., Pereira Valdez, M. J., Paz Viteri, B. S., & Rosero Duque, M. F. (2021). Gestión educativa: Factor clave en la implementación del currículo de educación física. Revista Venezolana de Gerencia, 26(5 Edición Especial), 232-247. https://doi.org/10.52080/rvgluz.26.e5.16
Saqr, M., & López-Pernas, S. (2023). The temporal dynamics of online problem-based learning: Why and when sequence matters. International Journal of Computer-Supported Collaborative Learning, 18(1), 11-37. https://doi.org/10.1007/s11412-023-09385-1
Sharif, H., & Atif, A. (2024). The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms. Education Sciences, 14(2), 176. https://doi.org/10.3390/educsci14020176
Shayan, P., & Zaanen, M. V. (2019). Predicting Student Performance from Their Behavior in Learning Management Systems. International Journal of Information and Education Technology, 9(5), 337-341. https://doi.org/10.18178/ijiet.2019.9.5.1223
Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366
Toribio Campos, Y. Y., Pacheco Ferreira, L. M., Posso Pacheco, C. J., Posso Pacheco, E. E., Salazar Ayala, J. J., & Arévalo Espinoza, O. M. (2025). Aprendizaje experiencial: El aula como escenario de innovación educativa. MENTOR revista de investigación educativa y deportiva, 4(1), 83-99. https://doi.org/10.56200/mried.v4i1.11295
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110. https://doi.org/10.1016/j.chb.2018.07.027
Wakjira, A., & Bhattacharya, S. (2021). Predicting Student Engagement in the Online Learning Environment: International Journal of Web-Based Learning and Teaching Technologies, 16(6), 1-21. https://doi.org/10.4018/IJWLTT.287095
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ximena Patricia León Quinapallo
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by-nc-nd/4.0
