Systematic map and review of Google Classroom usage during the Covid-19 pandemic: an analysis by data clustering approach
DOI:
https://doi.org/10.5753/rbie.2022.2204Keywords:
Google Classroom, Learning Management System, Machine Learning, Data Clustering, Covid-19 Pandemic, Systematic Literature ReviewAbstract
The Covid-19 pandemic brought changes in people's lives and in various sectors of the economy, including the education sector. In this context, students and teachers were able, through digital technologies, to continue their academic activities even in these difficult times. Many of these digital technologies were used to bridge the gap between instructors and students. Thus, in this work we reviewed the literature to better understand the usage of one of the widely known tools adopted by several schools: the Google Classroom (GC). For a more conclusive analysis, we used a systematic literature review to obtain relevant articles (N=25), and then, we grouped them by similarity using clustering algorithms to facilitate data analysis and summarization. The best clustering algorithm among the three compared was the k-medoids, as it generated clusters with better data separation. In our research, 56% of papers were published in 2020 and 44% of papers were published in 2021. Most of the articles reported positive reviews (72%) of using Google Classroom during the pandemic due to its good usability and the possibility of browsing through hyperlinks. Nevertheless, some weaknesses were found in 28% of papers (negative or neutral review) when teachers or students faced instability in the network. In addition, some authors have presented solutions to get around the problem about the GC usage during pandemic, such as the use of other networks to support learning and the use of tools that allow synchronous communication between teacher and student.
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