DESARROLLO DE UNA PLATAFORMA PARA EL ANÁLISIS DE LA FLUIDEZ LECTORA MEDIANTE MODELOS DE INTELIGENCIA ARTIFICIAL
Resumen
La fluidez lectora constituye un componente central de la competencia lectora, especialmente en los primeros años de la Educación Primaria, ya que articula decodificación, automaticidad y comprensión. En el campo de la evaluación educativa, la métrica Words Correct Per Minute (WCPM) se utiliza ampliamente en protocolos de Oral Reading Fluency (ORF) y Curriculum-Based Measurement (CBM). Este estudio tuvo como objetivo presentar el desarrollo tecnológico de una plataforma educativa destinada a la automatización de la evaluación de la fluidez lectora y al monitoreo del progreso de estudiantes de Educación Primaria, con base en la métrica WCPM y en recursos de inteligencia artificial. Se trata de una investigación aplicada de desarrollo tecnológico, de carácter descriptivo, centrada en la concepción, implementación y prueba funcional inicial de la herramienta. Los resultados indicaron viabilidad operativa para automatizar el cálculo de palabras correctas por minuto, identificar ocurrencias como pausas, repeticiones, omisiones y sustituciones, generar informes automáticos y organizar el seguimiento longitudinal del desempeño lector. También se observó una reducción preliminar del tiempo de análisis, de aproximadamente 10 a 15 minutos en la corrección manual a cerca de 1 minuto en el entorno digital. Se concluye que la herramienta representa una solución tecnológica prometedora para apoyar la evaluación de la fluidez lectora y el seguimiento pedagógico, aunque su confiabilidad, validez y aplicabilidad en contextos reales aún requieren investigación empírica sistemática.
Biografía del autor/a
Graduado em Geologia pela Universidade de São Paulo, foi pesquisador do Instituto de Pesquisas Tecnológicas do Estado de São Paulo (IPT), responsável pelo desenvolvimento de softwares nas áreas de Banco de Dados, Inteligência Artificial e Computação Gráfica. Atualmente é diretor da Caltech Informática Ltda. Autor de diversos livros sobre software representando o Brasil em evento de Tecnologia Educacional nos Estados Unidos em 1998, integrando a comitiva do Ministério da Educação.
Pedagoga e psicopedagoga, com atuação na Educação Básica como professora, coordenadora pedagógica e gestora escolar. Especialista em Psicopedagogia, Gestão Escolar, Programação Neurolinguística e Neurociência Aplicada à Alfabetização. Fundadora da MetaLer Academy, dedica-se ao desenvolvimento de pesquisas, programas formativos e projetos educacionais voltados à Educação Infantil e aos anos iniciais do Ensino Fundamental.
Referencias
ADAMS, M. J. Beginning to read: thinking and learning about print. Cambridge, MA: MIT Press, 1990.
BLACHMAN, B. A.; TANGEL, D. M.; BALL, E. W.; BLACK, R.; McGRAW, C. K. Developing phonological awareness and word recognition skills: a two-year intervention with low-income, inner-city children. Reading and Writing: An Interdisciplinary Journal, v. 11, p. 239-273, 1999.
BOLAÑOS, D.; COLE, R. A.; WARD, W. H.; TINDAL, G. A.; HASBROUCK, J.; SCHWANENFLUGEL, P. J. Human and automated assessment of oral reading fluency. Journal of Educational Psychology, v. 105, n. 4, p. 1142-1151, 2013 DOI: https://doi.org/10.1037/a0031479
BRASIL. Ministério da Educação. Painel Nacional de Leitura. Brasília, DF: MEC, 2019.
BRASIL. Ministério da Educação. Relatório Nacional de Alfabetização Baseada em Evidências (RENABE). Brasília, DF: MEC, 2021.
DENO, S. L. Curriculum-based measurement: the emerging alternative. Exceptional Children, v. 52, n. 3, p. 219-232, 1985 DOI: https://doi.org/10.1177/001440298505200303
DENO, S. L. Developments in curriculum-based measurement. The Journal of Special Education, v. 37, n. 3, p. 184-192, 2003 DOI: https://doi.org/10.1177/00224669030370030801
DENO, S. L.; MIRKIN, P. K.; CHIANG, B. Identifying valid measures of reading. Exceptional Children, v. 49, n. 1, p. 36-45, 1982 DOI: https://doi.org/10.1177/001440298204900105
DOWHOWER, S. Speaking of prosody: fluency’s unattended bedfellow. Theory Into Practice, v. 30, n. 3, p. 165-175, 1991.
FOORMAN, B. R.; FRANCIS, D. J.; FLETCHER, J. M.; SCHATSCHNEIDER, C.; MEHTA, P. The role of instruction in learning to read: preventing reading failure in at-risk children. Journal of Educational Psychology, v. 90, n. 1, p. 37-55, 1998.
FUCHS, L. S.; FUCHS, D. Monitoring student progress toward the development of reading competence: a review of three forms of classroom-based assessment. School Psychology Review, v. 28, p. 659-671, 1999.
FUCHS, L. S.; FUCHS, D.; HOSP, M. K.; JENKINS, J. R. Oral reading fluency as an indicator of reading competence: a theoretical, empirical, and historical analysis. Scientific Studies of Reading, v. 5, n. 3, p. 239-256, 2001.
GARZÓN, J.; PATIÑO, E.; MARULANDA, C. Systematic review of artificial intelligence in education: trends, benefits, and challenges. Multimodal Technologies and Interaction, v. 9, n. 8, p. 84, 2025 DOI: https://doi.org/10.3390/mti9080084
GLASGOW, A. et al. The development of oral reading fluency assessments through automatic speech recognition for students in grades 1-5. [S. l.]: WestEd, 2023.
HASBROUCK, J.; TINDAL, G. An update to compiled ORF norms. In: EUGENE, OR. Behavioral Research and Teaching. University of Oregon, 2017. (Technical Report, n. 1702).
HASBROUCK, J.; TINDAL, G. Oral reading fluency norms: a valuable assessment tool for reading teachers. The Reading Teacher, v. 59, n. 7, p. 636-644, 2006.
INSTITUTE OF EDUCATION SCIENCES. Measuring oral reading fluency with computerized oral reading evaluation (CORE). [S. l.]: Institute of Education Sciences, s. d. Disponível em: https://ies.ed.gov/use-work/awards/measuring-oral-reading-fluency-computerized-oral-reading-evaluation-core Acesso em: 16 mar. 2026.
KAMATA, A.; KARA, Y.; POTGIETER, C. J.; NESE, J. F. T. Estimating model-based oral reading fluency: a Bayesian approach. Educational and Psychological Measurement, v. 80, n. 5, p. 847-869, 2020 DOI: https://doi.org/10.1177/0013164419900208
KARA, Y.; KAMATA, A.; QIAO, X.; NESE, J. F. T. Equating oral reading fluency scores: a model-based approach. Educational and Psychological Measurement, v. 84, n. 1, p. 190-209, 2024 DOI: https://doi.org/10.1177/00131644221148122
MATHES, P. G.; HOWARD, J. K.; ALLEN, S. H.; FUCHS, D. Peer-assisted learning strategies for first-grade readers: responding to the needs of diverse learners. Reading Research Quarterly, v. 33, p. 62-94, 1998.
MELO-LÓPEZ, V.-A.; BASANTES-ANDRADE, A.; GUDIÑO-MEJÍA, C.-B.; HERNÁNDEZ-MARTÍNEZ, E. The impact of artificial intelligence on inclusive education: a systematic review. Education Sciences, v. 15, n. 5, p. 539, 2025 DOI: https://doi.org/10.3390/educsci15050539
NATIONAL READING PANEL. Teaching children to read: an evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. Washington, DC: National Institute of Child Health and Human Development, 2000.
NESE, J. F. T. et al. Accuracy of speech recognition in oral reading fluency for diverse student groups. [S. l.]: Behavioral Research and Teaching, 2020.
RASINSKI, T. V. Assessing reading fluency. Honolulu: Pacific Resources for Education and Learning, 2004.
RASINSKI, T. V. Fluency for everyone: incorporating fluency instruction in the classroom. The Reading Teacher, p. 690-693, 1989.
SHINN, M. R.; GOOD, R. H. Curriculum-based measurement of oral reading fluency: a confirmatory analysis of its relation to reading. School Psychology Review, v. 21, n. 3, p. 459-479, 1992.
STANOVICH, K. E. Progress in understanding reading: scientific foundations and new frontiers. New York: Guilford Press, 2000.
STANOVICH, K. E. Toward an interactive-compensatory model of individual differences in the development of reading fluency. Reading Research Quarterly, v. 16, p. 32-71, 1980.
STANOVICH, K. E.; STANOVICH, P. J. How research might inform the debate about early reading acquisition. Journal of Research in Reading, v. 18, p. 87-105, 1995.
TAN, X.; CHENG, G.; LING, M. H. Artificial intelligence in teaching and teacher professional development: a systematic review. Computers & Education: Artificial Intelligence, art. 100355, 2025 DOI: https://doi.org/10.1016/j.caeai.2024.100355
TORGESEN, J. K. Instructional interventions for children with reading disabilities. In: SHAPIRO, S.; ACCARDO, D.; CAPUTE, C. (eds.). Specific reading disability: a view of the spectrum. Parkton, MD: York Press, 1999. p. 197-220.
WISE, B. W.; OLSON, R. K. Computer-based phonological awareness and reading instruction. Annals of Dyslexia, v. 45, p. 99-122, 1995.
YERBABUENA TORRES, C. F.; VILLAGOMEZ CABEZAS, A. V.; YERBABUENA TORRES, A. R.; MENDOZA TORRES, N. A. Artificial intelligence tools applied to education: a systematic literature review. International Journal of Interactive Mobile Technologies (iJIM), v. 18, n. 24, p. 155-174, 2024 DOI: https://doi.org/10.3991/ijim.v18i24.50055
ZUTELL, J.; RASINSKI, T. V. Training teachers to attend to their students’ oral reading fluency. Theory Into Practice, v. 30, n. 3, p. 211-217, 1991.
Licencia
Derechos de autor 2026 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.