Letramento em dados na educação: mapeamento de tendências e lacunas em pesquisas acadêmicas
Resumo
Este artigo realiza um mapeamento sistemático sobre o letramento em dados na docência em contextos digitais. Foram analisados 12 artigos publicados entre 2014 e 2024, nas plataformas Google Acadêmico e ERIC, com uso da ferramenta Parsif.al1. Foram selecionados com base no modelo SPIDER e examinados por meio da Análise Temática de Braun e Clarke (2006). Os resultados apontam seis eixos principais, como o uso técnico dos dados, feedback digital, sense-making docente e barreiras institucionais. Observa-se uma predominância de abordagens instrumentais e pouca ênfase na formação crítica. Conclui-se que há necessidade de políticas formativas que articulem o uso de dados à prática pedagógica. O estudo contribui para ampliar o debate sobre a literacia crítica na educação digital.
Referências
ALVES, Lynn. Inteligência artificial e educação: refletindo sobre os desafios contemporâneos. [S.l.]: [s.n.], 2023.
ATIF, A.; RICHARDS, D.; BILGIN, A.; MARRONE, M. Learning analytics in higher education: a summary of tools and approaches. In: ASCILITE - AUSTRALIAN SOCIETY FOR COMPUTERS IN LEARNING IN TERTIARY EDUCATION ANNUAL CONFERENCE, 2013. Anais [...]. [S.l.]: Australasian Society for Computers in Learning in Tertiary Education, 2013. P. 68-72.
BALDWIN, S. J.; CHING, Y. H. Online course design: a review of the Canvas course evaluation checklist. The International Review of Research in Open and Distributed Learning, Athabasca, v. 20, n. 3, 2019.
BRAUN, V.; CLARKE, V. Using thematic analysis in psychology. Qualitative Research in Psychology, v. 3, n. 2, p. 77-101, 2006. DOI: 10.1191/1478088706qp063oa.
BUZATO, M. E. K. Letramentos digitais e formação de professores. In: CONGRESSO IBERO-AMERICANO EDUCAREDE, 3., 2006, São Paulo. Anais. São Paulo: CENPEC, 2006. P. 81-86.
CHATTI, M. A.; DYCKHOFF, A. L.; SCHROEDER, U.; THÜS, H. A reference model for learning analytics. International Journal of Technology Enhanced Learning, v. 4, n. 5-6, p. 318-331, 2012.
CRUZ, K; SANTAELLA, L. Jornalismo e Inteligência Artifical podem caminhar juntos? 1.ed. São Paulo: Estação das Letras e Cores, 2024.
COOKE, A.; SMITH, D.; BOOTH, A. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qualitative Health Research, v. 22, n. 10, p. 1435-1443, 2012. DOI: 10.1177/1049732312452938.
DATNOW, A., & HUBBARD, L. (2015). Teachers' use of assessment data to inform instruction: Lessons from the past and prospects for the future. Teachers College Record, 117(4), 1–26.
D’IGNAZIO, C.; BHARGAVA, R. Approaches to building big data literacy. Bloomberg Data for Good Exchange, 2015.
MANDINACH, E. B., & GUMMER, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376.
Mandinach, E. B., & Gummer, E. S. (2013). A systematic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37.
MANDINACH, E. B.; SCHILDKAMP, K. Misconceptions about data-based decision making in education: an exploration of the literature. Studies in Educational Evaluation, v. 69, p. 100842, 2021.
MARSH, J. A.; PANE, J. F.; HAMILTON, L. S. Making sense of data-driven decision making in education: evidence from recent RAND research. Occasional Paper. Santa Monica: Rand Corporation, 2006.
MATTAR, F. N. Pesquisa de marketing. São Paulo: Atlas, 1994.
METHLEY, A. M. Et al. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Research, v. 14, p. 579, 2014. DOI: 10.1186/s12913-014-0579-0.
MOHER, D. et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine, v. 6, n. 7, p. e1000097, 2009. DOI: 10.1371/journal.pmed.1000097. Acesso em: 8 jun. 2025.
PETERS, M. D. J. et al. Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, v. 13, n. 3, p. 141–146, 2015. Disponível em: [link]. Acesso em: 8 jun. 2025.
PETERSEN, K. Et al. Systematic mapping studies in software engineering. In: INTERNATIONAL CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING (EASE), 12., 2008, [S.l.]. Proceedings [...]. [S.l.: s.n.], 2008. P. 1-10.
PORTELA, A. M. Et al. Uso de learning analytics sobre dados de simulados para apoio à avaliação da aprendizagem por professores e gestores. RENOTE, v. 18, n. 2, p. 388-397, 2020.
RIDSALE, C. Et al. Strategies and best practices for data literacy education: knowledge synthesis report. [S.l.: s.n.], 2015.
SCHILDKAMP, K., POORTMAN, C. L., & HANDELZALTS, A. (2014). Data teams for school improvement. School Effectiveness and School Improvement, 25(2), 197–211.
SCHILDKAMP, K. Data-based decision-making for school improvement: research insights and gaps. Educational Research, v. 61, n. 3, p. 257-273, 2019.
SCHILDKAMP, K.; POORTMAN, C. Factors influencing the functioning of data teams. Teachers College Record, v. 117, n. 4, p. 1-42, 2015.
SELWYN, Neil. Educação e tecnologia: questões críticas. [S.l.]: [s.n.], 2017.
SOUZA, L. K. De. Pesquisa com análise qualitativa de dados: conhecendo a análise temática. Arquivos Brasileiros de Psicologia, Rio de Janeiro, v. 71, n. 2, p. 51-67, maio/ago. 2019.
WALDER, A. M. Pedagogical innovation in Canadian higher education: professors’ perspectives on its effects on teaching and learning. Studies in Educational Evaluation, v. 54, p. 71-82, 2017. DOI: 10.1016/j.stueduc.2016.11.001.
WELLS, C. S.; FAULKNER-BOND, M.; HAMBLETON, E. Educational measurement: from foundations to future. New York: Guilford Publications, 2016.
WILLIAMSON, Ben. Big Data in Education: The digital future of learning, policy and practice. London: SAGE, 2017
ZUBOFF, S. Big other: capitalismo de vigilância e perspectivas para uma civilização de informação. In: BRUNO, F. Et al. (Org.). Vigilância e visibilidade: espaço, tecnologia e subjetividade. [S.l.]: [s.n.], 2015. P. 17-68
ATIF, A.; RICHARDS, D.; BILGIN, A.; MARRONE, M. Learning analytics in higher education: a summary of tools and approaches. In: ASCILITE - AUSTRALIAN SOCIETY FOR COMPUTERS IN LEARNING IN TERTIARY EDUCATION ANNUAL CONFERENCE, 2013. Anais [...]. [S.l.]: Australasian Society for Computers in Learning in Tertiary Education, 2013. P. 68-72.
BALDWIN, S. J.; CHING, Y. H. Online course design: a review of the Canvas course evaluation checklist. The International Review of Research in Open and Distributed Learning, Athabasca, v. 20, n. 3, 2019.
BRAUN, V.; CLARKE, V. Using thematic analysis in psychology. Qualitative Research in Psychology, v. 3, n. 2, p. 77-101, 2006. DOI: 10.1191/1478088706qp063oa.
BUZATO, M. E. K. Letramentos digitais e formação de professores. In: CONGRESSO IBERO-AMERICANO EDUCAREDE, 3., 2006, São Paulo. Anais. São Paulo: CENPEC, 2006. P. 81-86.
CHATTI, M. A.; DYCKHOFF, A. L.; SCHROEDER, U.; THÜS, H. A reference model for learning analytics. International Journal of Technology Enhanced Learning, v. 4, n. 5-6, p. 318-331, 2012.
CRUZ, K; SANTAELLA, L. Jornalismo e Inteligência Artifical podem caminhar juntos? 1.ed. São Paulo: Estação das Letras e Cores, 2024.
COOKE, A.; SMITH, D.; BOOTH, A. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qualitative Health Research, v. 22, n. 10, p. 1435-1443, 2012. DOI: 10.1177/1049732312452938.
DATNOW, A., & HUBBARD, L. (2015). Teachers' use of assessment data to inform instruction: Lessons from the past and prospects for the future. Teachers College Record, 117(4), 1–26.
D’IGNAZIO, C.; BHARGAVA, R. Approaches to building big data literacy. Bloomberg Data for Good Exchange, 2015.
MANDINACH, E. B., & GUMMER, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376.
Mandinach, E. B., & Gummer, E. S. (2013). A systematic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37.
MANDINACH, E. B.; SCHILDKAMP, K. Misconceptions about data-based decision making in education: an exploration of the literature. Studies in Educational Evaluation, v. 69, p. 100842, 2021.
MARSH, J. A.; PANE, J. F.; HAMILTON, L. S. Making sense of data-driven decision making in education: evidence from recent RAND research. Occasional Paper. Santa Monica: Rand Corporation, 2006.
MATTAR, F. N. Pesquisa de marketing. São Paulo: Atlas, 1994.
METHLEY, A. M. Et al. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Research, v. 14, p. 579, 2014. DOI: 10.1186/s12913-014-0579-0.
MOHER, D. et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine, v. 6, n. 7, p. e1000097, 2009. DOI: 10.1371/journal.pmed.1000097. Acesso em: 8 jun. 2025.
PETERS, M. D. J. et al. Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, v. 13, n. 3, p. 141–146, 2015. Disponível em: [link]. Acesso em: 8 jun. 2025.
PETERSEN, K. Et al. Systematic mapping studies in software engineering. In: INTERNATIONAL CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING (EASE), 12., 2008, [S.l.]. Proceedings [...]. [S.l.: s.n.], 2008. P. 1-10.
PORTELA, A. M. Et al. Uso de learning analytics sobre dados de simulados para apoio à avaliação da aprendizagem por professores e gestores. RENOTE, v. 18, n. 2, p. 388-397, 2020.
RIDSALE, C. Et al. Strategies and best practices for data literacy education: knowledge synthesis report. [S.l.: s.n.], 2015.
SCHILDKAMP, K., POORTMAN, C. L., & HANDELZALTS, A. (2014). Data teams for school improvement. School Effectiveness and School Improvement, 25(2), 197–211.
SCHILDKAMP, K. Data-based decision-making for school improvement: research insights and gaps. Educational Research, v. 61, n. 3, p. 257-273, 2019.
SCHILDKAMP, K.; POORTMAN, C. Factors influencing the functioning of data teams. Teachers College Record, v. 117, n. 4, p. 1-42, 2015.
SELWYN, Neil. Educação e tecnologia: questões críticas. [S.l.]: [s.n.], 2017.
SOUZA, L. K. De. Pesquisa com análise qualitativa de dados: conhecendo a análise temática. Arquivos Brasileiros de Psicologia, Rio de Janeiro, v. 71, n. 2, p. 51-67, maio/ago. 2019.
WALDER, A. M. Pedagogical innovation in Canadian higher education: professors’ perspectives on its effects on teaching and learning. Studies in Educational Evaluation, v. 54, p. 71-82, 2017. DOI: 10.1016/j.stueduc.2016.11.001.
WELLS, C. S.; FAULKNER-BOND, M.; HAMBLETON, E. Educational measurement: from foundations to future. New York: Guilford Publications, 2016.
WILLIAMSON, Ben. Big Data in Education: The digital future of learning, policy and practice. London: SAGE, 2017
ZUBOFF, S. Big other: capitalismo de vigilância e perspectivas para uma civilização de informação. In: BRUNO, F. Et al. (Org.). Vigilância e visibilidade: espaço, tecnologia e subjetividade. [S.l.]: [s.n.], 2015. P. 17-68
Publicado
09/09/2025
Como Citar
SILVA, Amanda Valeria; ARAÚJO, Danilo Borges e Silva de.
Letramento em dados na educação: mapeamento de tendências e lacunas em pesquisas acadêmicas. In: CONGRESSO SOBRE TECNOLOGIAS NA EDUCAÇÃO (CTRL+E), 9. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 296-305.
DOI: https://doi.org/10.5753/ctrle.2025.12725.
