Conectividade Significativa na Educação Básica: Uma Revisão Sistemática para Governança e Tomada de Decisão Baseada em Evidências
Resumo
A conectividade significativa pode ser entendida como uma necessidade básica para a transformação digital, portanto, uma conexão de banda larga ilimitada, seja em casa, no local de trabalho ou em instituições de ensino é um importante fator para o acesso equitativo à Internet. O presente trabalho consiste em uma Revisão Sistemática da Literatura (RSL) compreendendo os anos de 2020 a 2024, com o objetivo de identificar frameworks, modelos e abordagens baseados em dados voltados à tomada de decisão em políticas públicas, conectividade significativa e transformação digital na educação. Foram incluídos vinte e dois trabalhos, com base nos critérios de seleção e qualidade, que destacam pesquisas em frameworks para políticas públicas, conectividade significativa e tomada de decisão baseada em dados.
Palavras-chave:
Conectividade Significativa, Políticas Públicas, Revisão Sistemática da Literatura
Referências
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Aly, E., Elsawah, S., e Ryan, M. J. (2022). Aligning the achievement of sdgs with long-term sustainability and resilience: An oobn modelling approach. Environmental Modelling & Software, 150:105360.
Amicone, A., Marangoni, L., Marceddu, A., e Miccoli, M. (2023). Ai-based public policy making: a new holistic, integrated and "ai by design" approach. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), pages 525–532. IEEE.
Arnaboldi, M. e Azzone, G. (2020). Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer. Heliyon, 6(6).
Aung, H. L. e Kham, N. S. M. (2023). A conceptual framework for ict policy development in myanmar education sector. In 2023 IEEE Conference on Computer Applications (ICCA), pages 417–422.
Baig, M. I., Shuib, L., e Yadegaridehkordi, E. (2021). A model for decision-makers' adoption of big data in the education sector. Sustainability, 13(24):13995.
Barbosa, A. e Castello, G. (2025). Para além do acesso à internet: como garantir a conectividade significativa. Computação Brasil, (53):43–49.
Conejero, J. M., Preciado, J. C., Fernández-García, A. J., Prieto, A. E., e Rodríguez-Echeverría, R. (2021). Towards the use of data engineering, advanced visualization techniques and association rules to support knowledge discovery for public policies. Expert Systems with Applications, 170:114509.
Cruz, A., Pinheiro, E., Sousa, S., Rodrigues, E., Andrade, R. M., e Macedo, J. A. (2024). Conectividade na educação do brasil: Estratégias nacionais e internacionais para melhorar a infraestrutura de internet das escolas. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 404–417, Porto Alegre, RS, Brasil. SBC.
De Carvalho, M. S. e Da Silva, G. L. (2021). Inside the black box: using explainable ai to improve evidence-based policies. In 2021 IEEE 23rd Conference on Business Informatics (CBI), volume 2, pages 57–64. IEEE.
Del-Valle-Soto, C., Briseño, R. A., López-Pimentel, J.-C., Velázquez, R., Valdivia, L. J., e Varela-Aldás, J. (2024). Bridging the digital divide in mexico: A critical analysis of telecommunications infrastructure and predictive models for policy innovation. In Telecom, volume 5, pages 1076–1101. MDPI.
Fu, H., Fu, L., Dávid, L. D., Zhong, Q., e Zhu, K. (2024). Bridging gaps towards the 2030 agenda: a data-driven comparative analysis of government and public engagement in china towards achieving sustainable development goals. Land, 13(6):818.
Giga (2024). Connected education internet measurement system – giga. Accessado em 26 de junho de 2024.
(ITU), I. T. U. (2024). Universal and meaningful connectivity: A framework for indicators and metrics. Accessed: 2025-03-14.
ITU, U. N. (2021). Achieving universal and meaningful digital connectivity setting a baseline and targets for 2030. Acessado em 26 de junho de 2024.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, ver. 2.3 ebse technical report. ebse.
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Kuziemski, M. e Misuraca, G. (2020). Ai governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6):101976.
Kyriazis, D., Biran, O., Bouras, T., Brisch, K., Duzha, A., Del Hoyo, R., Kiourtis, A., Kranas, P., Maglogiannis, I., Manias, G., et al. (2020). Policycloud: analytics as a service facilitating efficient data-driven public policy management. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part I 16, pages 141–150. Springer.
Lnenicka, M., Kopackova, H., Machova, R., e Komarkova, J. (2020). Big and open linked data analytics: a study on changing roles and skills in the higher educational process. International Journal of Educational Technology in Higher Education, 17:1–30.
Lněnička, M. e Máchová, R. (2022). A theoretical framework to evaluate ict disparities and digital divides: Challenges and implications for e-government development.
Maffei, S., Leoni, F., e Villari, B. (2020). Data-driven anticipatory governance. emerging scenarios in data for policy practices. Policy Design and Practice, 3(2):123–134.
Nimer, K., Uyar, A., Kuzey, C., e Schneider, F. (2022). E-government, education quality, internet access in schools, and tax evasion. Cogent Economics & Finance, 10(1):2044587.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., e Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372.
Palacios Abad, B., Belding, E., Vigil-Hayes, M., e Zegura, E. (2024). Mending the fabric: the contentious, collaborative work of repairing broadband maps. 8(CSCW2).
Papadakis, T., Christou, I. T., Ipektsidis, C., Soldatos, J., e Amicone, A. (2024). Explainable and transparent artificial intelligence for public policymaking. Data & Policy, 6:e10.
Parekh, J., Parekh, C., e Ghasemi, A. (2022). Enabling universal connectivity via data-driven policymaking: A north american case study. IEEE Communications Magazine, 59(12):23–29.
Pata, K., Tammets, K., Väljataga, T., Kori, K., Laanpere, M., e Rõbtsenkov, R. (2022). The patterns of school improvement in digitally innovative schools. Technology, Knowledge and Learning, 27(3):823–841.
Queiroga, E. M., Siqueira, E. S., dos Santos Portela, C., Cordeiro, T. D., Bittencourt, I. I., Isotani, S., Melo, R. F., Muñoz, R., e Cechinel, C. (2024). Data-driven strategies for achieving school equity: Insights from brazil and policy recommendations. IEEE Access.
Shah, S. I. H., Peristeras, V., e Magnisalis, I. (2024). A conceptual framework for the government big data ecosystem ('datagov. eco'). Data & Knowledge Engineering, 154:102348.
Stavropoulou, S., Romas, I., Tsekeridou, S., Loutsaris, M. A., Lampoltshammer, T., Thurnay, L., Virkar, S., Schefbeck, G., Kyriakou, N., Lachana, Z., et al. (2020). Architecting an innovative big open legal data analytics, search and retrieval platform. In Proceedings of the 13th international conference on theory and practice of electronic governance, pages 723–730.
Wirajing, M. A. K. e Nchofoung, T. N. (2023). The role of education in modulating the effect of ict on governance in africa. Education and Information Technologies, 28(9):11987–12020.
Zain, Y. M., Yaacob, S., Ibrahim, R., e Hussein, S. S. (2023). Valuable insights framework for big data and analytics in the malaysian public sector organization. In 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pages 1–10.
Aly, E., Elsawah, S., e Ryan, M. J. (2022). Aligning the achievement of sdgs with long-term sustainability and resilience: An oobn modelling approach. Environmental Modelling & Software, 150:105360.
Amicone, A., Marangoni, L., Marceddu, A., e Miccoli, M. (2023). Ai-based public policy making: a new holistic, integrated and "ai by design" approach. In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), pages 525–532. IEEE.
Arnaboldi, M. e Azzone, G. (2020). Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer. Heliyon, 6(6).
Aung, H. L. e Kham, N. S. M. (2023). A conceptual framework for ict policy development in myanmar education sector. In 2023 IEEE Conference on Computer Applications (ICCA), pages 417–422.
Baig, M. I., Shuib, L., e Yadegaridehkordi, E. (2021). A model for decision-makers' adoption of big data in the education sector. Sustainability, 13(24):13995.
Barbosa, A. e Castello, G. (2025). Para além do acesso à internet: como garantir a conectividade significativa. Computação Brasil, (53):43–49.
Conejero, J. M., Preciado, J. C., Fernández-García, A. J., Prieto, A. E., e Rodríguez-Echeverría, R. (2021). Towards the use of data engineering, advanced visualization techniques and association rules to support knowledge discovery for public policies. Expert Systems with Applications, 170:114509.
Cruz, A., Pinheiro, E., Sousa, S., Rodrigues, E., Andrade, R. M., e Macedo, J. A. (2024). Conectividade na educação do brasil: Estratégias nacionais e internacionais para melhorar a infraestrutura de internet das escolas. In Anais do XXXV Simpósio Brasileiro de Informática na Educação, pages 404–417, Porto Alegre, RS, Brasil. SBC.
De Carvalho, M. S. e Da Silva, G. L. (2021). Inside the black box: using explainable ai to improve evidence-based policies. In 2021 IEEE 23rd Conference on Business Informatics (CBI), volume 2, pages 57–64. IEEE.
Del-Valle-Soto, C., Briseño, R. A., López-Pimentel, J.-C., Velázquez, R., Valdivia, L. J., e Varela-Aldás, J. (2024). Bridging the digital divide in mexico: A critical analysis of telecommunications infrastructure and predictive models for policy innovation. In Telecom, volume 5, pages 1076–1101. MDPI.
Fu, H., Fu, L., Dávid, L. D., Zhong, Q., e Zhu, K. (2024). Bridging gaps towards the 2030 agenda: a data-driven comparative analysis of government and public engagement in china towards achieving sustainable development goals. Land, 13(6):818.
Giga (2024). Connected education internet measurement system – giga. Accessado em 26 de junho de 2024.
(ITU), I. T. U. (2024). Universal and meaningful connectivity: A framework for indicators and metrics. Accessed: 2025-03-14.
ITU, U. N. (2021). Achieving universal and meaningful digital connectivity setting a baseline and targets for 2030. Acessado em 26 de junho de 2024.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, ver. 2.3 ebse technical report. ebse.
Kitchenham, B. e Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, Keele University and Durham University.
Kuziemski, M. e Misuraca, G. (2020). Ai governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6):101976.
Kyriazis, D., Biran, O., Bouras, T., Brisch, K., Duzha, A., Del Hoyo, R., Kiourtis, A., Kranas, P., Maglogiannis, I., Manias, G., et al. (2020). Policycloud: analytics as a service facilitating efficient data-driven public policy management. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part I 16, pages 141–150. Springer.
Lnenicka, M., Kopackova, H., Machova, R., e Komarkova, J. (2020). Big and open linked data analytics: a study on changing roles and skills in the higher educational process. International Journal of Educational Technology in Higher Education, 17:1–30.
Lněnička, M. e Máchová, R. (2022). A theoretical framework to evaluate ict disparities and digital divides: Challenges and implications for e-government development.
Maffei, S., Leoni, F., e Villari, B. (2020). Data-driven anticipatory governance. emerging scenarios in data for policy practices. Policy Design and Practice, 3(2):123–134.
Nimer, K., Uyar, A., Kuzey, C., e Schneider, F. (2022). E-government, education quality, internet access in schools, and tax evasion. Cogent Economics & Finance, 10(1):2044587.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., e Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372.
Palacios Abad, B., Belding, E., Vigil-Hayes, M., e Zegura, E. (2024). Mending the fabric: the contentious, collaborative work of repairing broadband maps. 8(CSCW2).
Papadakis, T., Christou, I. T., Ipektsidis, C., Soldatos, J., e Amicone, A. (2024). Explainable and transparent artificial intelligence for public policymaking. Data & Policy, 6:e10.
Parekh, J., Parekh, C., e Ghasemi, A. (2022). Enabling universal connectivity via data-driven policymaking: A north american case study. IEEE Communications Magazine, 59(12):23–29.
Pata, K., Tammets, K., Väljataga, T., Kori, K., Laanpere, M., e Rõbtsenkov, R. (2022). The patterns of school improvement in digitally innovative schools. Technology, Knowledge and Learning, 27(3):823–841.
Queiroga, E. M., Siqueira, E. S., dos Santos Portela, C., Cordeiro, T. D., Bittencourt, I. I., Isotani, S., Melo, R. F., Muñoz, R., e Cechinel, C. (2024). Data-driven strategies for achieving school equity: Insights from brazil and policy recommendations. IEEE Access.
Shah, S. I. H., Peristeras, V., e Magnisalis, I. (2024). A conceptual framework for the government big data ecosystem ('datagov. eco'). Data & Knowledge Engineering, 154:102348.
Stavropoulou, S., Romas, I., Tsekeridou, S., Loutsaris, M. A., Lampoltshammer, T., Thurnay, L., Virkar, S., Schefbeck, G., Kyriakou, N., Lachana, Z., et al. (2020). Architecting an innovative big open legal data analytics, search and retrieval platform. In Proceedings of the 13th international conference on theory and practice of electronic governance, pages 723–730.
Wirajing, M. A. K. e Nchofoung, T. N. (2023). The role of education in modulating the effect of ict on governance in africa. Education and Information Technologies, 28(9):11987–12020.
Zain, Y. M., Yaacob, S., Ibrahim, R., e Hussein, S. S. (2023). Valuable insights framework for big data and analytics in the malaysian public sector organization. In 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pages 1–10.
Publicado
05/10/2026
Como Citar
CRUZ, Antonia Raiane S. A.; PINHEIRO, Everson N.; RODRIGUES, Emanuel B.; ANDRADE, Rossana Maria C.; FERNANDES, Carlos Estêvão R.; DE MACÊDO, José Antonio F..
Conectividade Significativa na Educação Básica: Uma Revisão Sistemática para Governança e Tomada de Decisão Baseada em Evidências. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1549-1563.
DOI: https://doi.org/10.5753/sbie.2026.27941.
