Interpretación de datos estadísticos en la evaluación del aprendizaje: de la medición al juicio docente
DOI:
https://doi.org/10.70208/3007.8245.v6.n3.555Palabras clave:
Interpretación, datos estadísticos, evaluación del aprendizaje, medición, juicio docenteResumen
El objetivo de este estudio fue analizar la interpretación de los datos estadísticos en la evaluación del aprendizaje como proceso que permite trascender la medición contribuyendo a la construcción del juicio docente. Metodológicamente, se realizó una revisión crítica-documental de 112 publicaciones, de las cuales 25 fuentes fueron seleccionadas mediante criterios de inclusión y exclusión, con temporalidad 2021-2026, de artículos e investigaciones científicas, reportes de organismos internacionales sobre alfabetización de datos, evaluación formativa, retroalimentación y gobernanza de datos educativos. El análisis se organizó en seis núcleos de problematización: límites de la medición, lectura de la distribución y la variabilidad, relación entre datos y evaluación formativa, alfabetización estadística docente, riesgos de sobre interpretación y criterios para toma de decisiones pedagógicas responsable. Como resultados se consideró: los promedios, porcentajes, tasas de aprobación, niveles de logro y visualizaciones son descriptores útiles, pero insuficientes si se desvinculan del propósito de aprendizaje, calidad del instrumento, diversidad del grupo y trayectoria individual. Como conclusiones teóricas se obtuvo que los datos solo adquieren valor educativo cuando se leen en contexto, se contrastan con otras evidencias y se traducen en decisiones formativas. Por lo que se propone comprender la estadística evaluativa como mediación para el razonamiento pedagógico, y no como sustituto de este.
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Alonzo, D., Quimno, V., Townend, G., y Oo, C. (2024). Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023). Educational Assessment, Evaluation and Accountability, 36, 433–451. https://doi.org/10.1007/s11092-024-09443-8
Atjonen, P., Pöntinen, S., Kontkanen, S., y Ruotsalainen, P. (2022). In enhancing preservice teachers’ assessment literacy: Focus on knowledge base, conceptions of assessment, and teacher learning. Frontiers in Education, 7, 891391. https://doi.org/10.3389/feduc.2022.891391
Børte, K., Lillejord, S., Chan, J., Wasson, B., y Greiff, S. (2023). Prerequisites for teachers’ technology use in formative assessment practices: A systematic review. Educational Research Review, 41, 100568. https://doi.org/10.1016/j.edurev.2023.100568
Conn, C.., Bohan, K., Bies-Hernandez, N., Powell, P., Sweeny, S., Persinger, L., y Persinger, J. (2022). Expected data literacy knowledge and skills for early career teachers: Perspectives from school and district personnel. Teaching and Teacher Education, 111, 103607. https://doi.org/10.1016/j.tate.2021.103607
Cui, Y., y Zhang, H. (2022). Integrating teacher data literacy with TPACK: A self-report study based on a novel framework for teachers’ professional development. Frontiers in Psychology, 13, 966575. https://doi.org/10.3389/fpsyg.2022.966575
Dignath, C., van Ewijk, R., Perels, F., y Fabriz, S. (2023). Let learners monitor the learning content and their learning behavior! A meta-analysis on the effectiveness of tools to foster monitoring. Educational Psychology Review, 35(2), 62. https://doi.org/10.1007/s10648-023-09718-4
Friedrich, A., Schreiter, S., Vogel, M., Becker-Genschow, S., Brünken, R., Kuhn, J., Lehmann, J., y Malone, S. (2024). What shapes statistical and data literacy research in K-12 STEM education? A systematic review of metrics and instructional strategies. International Journal of STEM Education, 11, 58. https://doi.org/10.1186/s40594-024-00517-z
Jungjohann, J., Gebhardt, M., y Scheer, D. (2022). Understanding and improving teachers’ graph literacy for data-based decision-making via video intervention. Frontiers in Education, 7, 919152. https://doi.org/10.3389/feduc.2022.919152
Lee, J., Alonzo, D., Beswick, K., Abril, J. M. V., Chew, A. W., y Oo, C. Z. (2024). Dimensions of teachers’ data literacy: A systematic review of literature from 1990 to 2021. Educational Assessment, Evaluation and Accountability, 36, 145–200. https://doi.org/10.1007/s11092-024-09435-8
Mandinach, E. B., y Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842. https://doi.org/10.1016/j.stueduc.2020.100842
Michos, K., Schmitz, M.-L., & Petko, D. (2023). Teachers’ data literacy for learning analytics: A central predictor for digital data use in upper secondary schools. Education and Information Technologies, 28, 14453–14471. https://doi.org/10.1007/s10639-023-11772-y.
Miller-Bains, K. L., Cohen, J., & Wong, V. C. (2022). Developing data literacy: Investigating the effects of a pre-service data use intervention. Teaching and Teacher Education, 109, 103569. https://doi.org/10.1016/j.tate.2021.103569
OECD. (2023). OECD digital education outlook 2023: Towards an effective digital education ecosystem. OECD Publishing. https://doi.org/10.1787/c74f03de-en
OECD. (2025). Unlocking high-quality teaching. OECD Publishing. https://doi.org/10.1787/f5b82176-en
Panadero, E., Jonsson, A., Pinedo, L., y Fernández-Castilla, B. (2023). Effects of rubrics on academic performance, self-regulated learning, and self-efficacy: A meta-analytic review. Educational Psychology Review, 35, 113. https://doi.org/10.1007/s10648-023-09823-4
Papamitsiou, Z., Filippakis, M. E., Poulou, M., Sampson, D., Ifenthaler, D., y Giannakos, M. (2021). Towards an educational data literacy framework: Enhancing the profiles of instructional designers and e-tutors of online and blended courses with new competences. Smart Learning Environments, 8, 18. https://doi.org/10.1186/s40561-021-00163-w
Pastore, S. (2023). Teacher assessment literacy: A systematic review. Frontiers in Education, 8, 1217167. https://doi.org/10.3389/feduc.2023.1217167
Sandoval-Ríos, F., Gajardo-Poblete, C., y López-Núñez, J. A. (2025). Role of data literacy training for decision-making in teaching practice: A systematic review. Frontiers in Education, 10, 1485821. https://doi.org/10.3389/feduc.2025.1485821
Schreiter, S., Friedrich, A., Fuhr, H., Malone, S., Brünken, R., Kuhn, J., & Vogel, M. (2024). Teaching for statistical and data literacy in K-12 STEM education: A systematic review on teacher variables, teacher education, and impacts on classroom practice. ZDM Mathematics Education, 56, 31–45. https://doi.org/10.1007/s11858-023-01531-1
Sjunnesson, H. (2022). Teachers’ alignment between a local initiated external assessment: The diagnostic literacy test—and teaching regarding special educational needs students’ needs. Frontiers in Education, 7, 1075165. https://doi.org/10.3389/feduc.2022.1075165
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
UNESCO. (2025). Supporting schools’ and teachers’ use of assessment to inform learning of all students. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000388275
Von Hagen, A., Balbi, A., Bonilla, M., & Arbildi, C. (2025). What do we mean when we talk about teacher professional development in formative assessment? A systematic review. International Journal of Educational Research, 131, 102586. https://doi.org/10.1016/j.ijer.2025.102586
Yan, Z., Li, Z., Panadero, E., Yang, M., Yang, L., y Lao, H. (2021). A systematic review on factors influencing teachers’ intentions and implementations regarding formative assessment. Assessment in Education: Principles, Policy & Practice, 28(3), 228–260. https://doi.org/10.1080/0969594X.2021.1884042
Yan, Z., y Pastore, S. (2022). Are teachers literate in formative assessment?. The development and validation of the Teacher Formative Assessment Literacy Scale. Studies in Educational Evaluation, 74, 101183. https://doi.org/10.1016/j.stueduc.2022.101183
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Derechos de autor 2026 Luis Humberto Pérez

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.













