RURAL EDUCATION AND SOCIAL JUSTICE: CONTEXTUALIZED PEDAGOGIES FOR RURAL COMMUNITIES
DOI:
https://doi.org/10.66104/w3vnja02Keywords:
educação no campo; justiça social; pedagogia contextualizada; comunidades rurais; inclusãoAbstract
Rural education, historically neglected by urban-centric models, demands a reconfiguration based on social justice and pedagogical contextualization. This study, designed as a theoretical essay grounded in an integrative literature review, analyzes how contextualized pedagogies can transform the reality of rural communities. The central objective is to investigate the effectiveness of Decision Engineering in designing educational solutions that respect rural identity while optimizing the learning flow. The methodology followed the six-stage protocol for integrative reviews, with an analysis of evidence collected between 2021 and 2026. The results demonstrate that the integration of data intelligence and decentralized governance architectures allows for mitigating dropout rates and promoting radical inclusion. It is concluded that the competitive advantage of rural schools lies in the ability to transform traditional knowledge into knowledge assets supported by an ethical and technologically robust infrastructure.
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References
BARDIN, L. Análise de Conteúdo. São Paulo: Edições 70, 2016.
CAGAN, M. INSPIRED: How to Create Tech Products Customers Love. 2. ed. New Jersey: Wiley, 2018.
DAMA International. DAMA-DMBOK2: Data Management Body of Knowledge. 2. ed. New Jersey: Technics Publications, 2017.
DAVENPORT, T. H.; HARRIS, J. G. Competing on Analytics: The New Science of Winning. Boston: Harvard Business Review Press, 2007.
DEHGHANI, Z. Data Mesh: Delivering Data-Driven Value at Scale. Sebastopol: O’Reilly, 2022.
FRANÇA, J. L. Manual para normalização de publicações técnico-científicas. 8. ed. Belo Horizonte: Ed. UFMG, 2008.
GANDOMI, A.; HAIDER, M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, v. 35, n. 2, p. 137-144, 2015. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
KREUZBERGER, R.; KÜHL, D.; POLZE, J. MLOps: a survey of techniques for operationalizing machine learning. ACM Computing Surveys, 2023.
LEWIS, P. et al. Retrieval-augmented generation for knowledge-intensive NLP. NeurIPS, 2020.
NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY (NIST). AI Risk Management Framework 1.0. Gaithersburg: NIST, 2023.
PROVOST, F.; FAWCETT, T. Data Science for Business. Sebastopol: O’Reilly, 2013.
SCULLEY, D. et al. Hidden technical debt in machine learning systems. NIPS, p. 2503-2511, 2015.
SOUZA, M. T.; SILVA, M. D.; CARVALHO, R. Revisão integrativa: o que é e como fazer. Einstein, v. 8, n. 1, p. 102-106, 2010. DOI: https://doi.org/10.1590/s1679-45082010rw1134
WESTERMAN, G.; BONNET, D.; MCAFEE, A. Leading Digital: Turning Technology into Business Transformation. Boston: Harvard Business Review Press, 2014.
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Copyright (c) 2026 Cristiane do Socorro Rebelo Pamplona, Ana Paula Viana Amurim, Lailton da Silva Freire, Filiphe Chagas de Lucas, Júnior de Carvalho e Souza, Marcio Harrison dos Santos Ferreira, Alexsandro da Silva Cavalcanti, Paulo Henrique Cabral Arantes, João Emílio Alves da Costa, Roberta Alves da Silva Ferreira

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