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The field of Artificial Intelligence (AI) has the potential to improve teaching and learning, for example, by analyzing data produced in educational environments. Furthermore, it can also worsen inequality, as it requires students and instructors to have access to the infrastructure (smartphones or computers) required by the majority of those tools to generate and analyse data. However, access to such infrastructure is not a reality for many students around the world. To shine a light on this problem, this paper investigates, through a Systematic Mapping Study (SMS), initiatives that enable a more inclusive data analysis using AI in education, especially in scenarios with few connectivity resources. We identified that these initiatives are scarce and they are focused in the first phase of the data analysis task: the data collection. Based on the SMS results, we propose a set of recommendations for researchers to offer directions toward a more inclusive analysis of educational data using AI. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flow Theory has been increasingly applied to Computers and Education to address several topics within this field (e.g., motivation, engagement, learning performance and so on). At the same time, in comparison with other recent theories related to Computers and Education, (e.g., Self-Determination Theory, S-Curve Theory, Intrinsic motivation, etc.), is a young topic, with different open research questions. Additionally, the Computers and Education community still lacks a comprehensive understanding of how Flow Theory is used in the area. Thus, this paper presents a Systematic Literature Review aiming to identify how students' flow state are measured during learning activities, how such activities are designed, which are the flow models used in Computers and Education and which are the main benefits of being in the flow state for the students. The main findings of this work are: (1) there is positive evidence about the benefits of applying Flow Theory in Computers and Education, especially, for increasing students' learning, to generate students' satisfaction, and to enable exploratory behavior; (2) the majority of studies use questionnaires to manual identify students' flow state; (3) a great diversity of flow state scales have been used; (4) the majority of studies are not designing activities for leading students to the flow state, and (5) the Csikszentmihalyi' flow model is more used. Finally, we conclude this work by showing some promising and interesting research opportunities that are underexplored in current research and practice. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Informática na Educação is the property of Revista Brasileira de Informatica na Educacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)