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¿Deben utilizarse los sistemas de observación docente para la toma de decisiones de alto impacto?

Autores/as

DOI:

https://doi.org/10.14507/epaa.34.9841

Palabras clave:

observación docente, decisiones de alto impacto, inferencia

Resumen

Este estudio cuestiona la idoneidad de los datos provenientes de la observación de la enseñanza para la toma de decisiones de alto impacto que afectan al profesorado. Definimos la idoneidad como el grado en que los propósitos previstos se logran sin causar daños indebidos y sostenemos que esta depende fundamentalmente de las propiedades técnicas de los datos producidos por un sistema de observación, las cuales, a su vez, dependen de los atributos diseñados en dicho sistema. Realizamos un experimento para comprender mejor la relación entre los atributos de los sistemas de observación docente y la idoneidad de los datos que generan. Comparamos tres sistemas con atributos diferentes, incluidos instrumentos de rúbricas que imponen distintas cargas inferenciales a los evaluadores. Evaluadores con experiencia fueron asignados aleatoriamente a un sistema y recibieron capacitación adecuada. Posteriormente, evaluaron la enseñanza de candidatos avanzados a docentes mediante la visualización de videos de sus clases. Consideramos tres criterios para valorar los datos resultantes: la capacidad predictiva de la contribución del docente al aprendizaje estudiantil, la correlación de las puntuaciones entre sistemas y el grado de acuerdo entre evaluadores dentro de cada sistema. Encontramos que un sistema con baja carga inferencial (junto con otros atributos) superó a los sistemas con mayores cargas inferenciales; sin embargo, aun así podría resultar insuficiente para tomar decisiones de alto impacto con plena confianza. Sostenemos que pocos, si es que alguno, de los sistemas de observación ampliamente utilizados cumplen con este estándar.

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Biografía del autor/a

Michael Strong, Texas Tech University

Michael Strong is a research scientist in the College of Education at Texas Tech University. He is the former Director of Research at the New Teacher Center at the University of California, Santa Cruz, and at the Center on Deafness at the University of California, San Francisco. His current interest centers on the observation of teaching behavior and the performance of teacher pay-for-performance programs.

Jaehoon Lee, Texas Tech University

Jaehoon Lee, Ph.D., is an associate professor of educational psychology at Texas Tech University. His scholarly expertise spans advanced research methodologies, statistical modeling techniques, and measurement instruments applied across education, psychology, public health, and related disciplines. His recent work focuses on the evaluation and application of mixed-effects models, mixture models, Bayesian approaches, and propensity score methods for analyzing complex data sets.

John Gargani, Gargani + Co.

John Gargani is president of Gargani + Co., a company that helps organizations around the world achieve their social and environmental missions by measuring their impact, designing new programs, and improving performance. He is an evaluator, professor, speaker, and writer with nearly 30 years of experience directing evaluations of every type.

Minju Yi, Texas Tech University

Dr. Minju Yi is an assistant professor of practice in the Department of Teacher Education at Texas Tech University. Her research is informed by classroom teaching, professional practice, and policy work in mathematics teacher preparation and development. Her scholarship focuses on: (1) developing and evaluating interventions to strengthen preservice teachers’ mathematical content knowledge and pedagogical skills; (2) investigating how teachers’ mathematical understanding translates into instructional practice; (3) designing curriculum and instructional modules that integrate STEM disciplines to enhance both teacher development and student learning; and (4) examining the impact of teacher evaluation systems on teaching quality to inform equitable, evidence-based policy decisions.

Hyunjin Shim, Texas Tech University

Hyunjin Shim, Ph.D., is an associate researcher specializing in educational research on the development of knowledge and skills among preservice mathematics teachers. Her scholarly interests include curriculum and instruction, teacher preparation, and evidence-based approaches to mathematics education. She brings decades of experience as an elementary school teacher, which informs and strengthens her research.

Hyunchang Moon, Augusta University

Dr. Hyunchang (Henry) Moon is an assistant professor of pediatrics at the Medical College of Georgia, Augusta University, and a senior editor for the Canadian Medical Education Journal. His scholarly work centers on learning design, educational research, and the integration of AI and emerging technologies, with a strong emphasis on evidence-based and learner-centered approaches. He contributes to the design and continuous improvement of medical curricula, supports the effective and ethical use of educational technologies, and collaborates with educators and researchers to advance teaching quality and learning outcomes.

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Publicado

2026-04-07

Cómo citar

Strong, M., Lee, J., Gargani, J., Yi, M., Shim, H., & Moon, H. (2026). ¿Deben utilizarse los sistemas de observación docente para la toma de decisiones de alto impacto?. Archivos Analíticos De Políticas Educativas, 34. https://doi.org/10.14507/epaa.34.9841

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