Próximo(s)

Os sistemas de observação docente devem ser utilizados para a tomada de decisões de alto impacto?

Autores

DOI:

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

Palavras-chave:

observação docente, decisões de alto impacto, inferência

Resumo

Este estudo questiona a adequação dos dados de observação da prática docente para a tomada de decisões de alto impacto que afetam professores. Definimos adequação como o grau em que os propósitos pretendidos são alcançados sem causar danos indevidos e argumentamos que ela depende fundamentalmente das propriedades técnicas dos dados produzidos por um sistema de observação, as quais, por sua vez, dependem dos atributos incorporados ao seu desenho. Conduzimos um experimento para compreender melhor a relação entre os atributos dos sistemas de observação docente e a adequação dos dados por eles gerados. Comparamos três sistemas com atributos distintos, incluindo rubricas que impõem diferentes níveis de carga inferencial aos avaliadores. Avaliadores experientes foram designados aleatoriamente a um dos sistemas e devidamente treinados. Em seguida, avaliaram a prática de candidatos avançados à docência por meio da observação de vídeos de suas aulas. Consideramos três critérios para julgar os dados resultantes: o poder de prever a contribuição do professor para a aprendizagem dos estudantes, a correlação das pontuações entre os sistemas e o grau de concordância entre avaliadores dentro de cada sistema. Constatamos que um sistema com baixa carga inferencial (associada a outros atributos) superou aqueles com cargas inferenciais mais elevadas; ainda assim, pode não ser suficiente para sustentar decisões de alto impacto com segurança. Sustentamos que poucos, se é que algum, dos sistemas de observação amplamente utilizados atendem a esse padrão.

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Biografia do Autor

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

Como Citar

Strong, M., Lee, J., Gargani, J., Yi, M., Shim, H., & Moon, H. (2026). Os sistemas de observação docente devem ser utilizados para a tomada de decisões de alto impacto?. Arquivos Analíticos De Políticas Educativas, 34. https://doi.org/10.14507/epaa.34.9841

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