Resumo

HUGO, Wayne. Probabilistic professional judgement in teaching. Journal of Education [online]. 2025, n.99, pp.3-27. ISSN 2520-9868.  https://doi.org/10.17159/2520-9868/i99a01.

Subjective Bayesian reasoning offers a framework for understanding how teachers actively refine their professional judgement in response to the inherent uncertainties of the classroom. Drawing on Luhmann's systems theory, Simon's bounded rationality, and Shalem's work on professional knowledge, in this paper I demonstrate how Bayesian reasoning models how teachers navigate three fundamental challenges: the operational separation between teaching and learning systems (creating inherent unpredictability); cognitive limitations that necessitate satisficing solutions (efficient ways to predict and decide); and the systematic development of professional knowledge through academic and diagnostic classifications (building the basis for better predictions). Through constructed scenarios, I demonstrate how novice teachers often begin with fragile priors based on theoretical knowledge and personal experience, which undergo dramatic updates when confronted with the mismatch between expectations and classroom realities (significant prediction errors). As teachers gain experience, they develop more robust and refined priors-belief systems that can incorporate new evidence while maintaining stable overall patterns, reflecting increasingly sophisticated predictive models. This evolution reflects the development of diagnostic classifications that guide professional decision-making. I show that subjective Bayesian reasoning provides a formal mechanism for modelling belief updating in professional judgement. While teachers may not engage in explicit probabilistic calculations, I argue that subjective Bayesian reasoning underlies the development of fast and frugal heuristics that become increasingly expert predictive tools with experience. By integrating Bayesian reasoning with established theories of professional knowledge development, a theoretical framework is offered that uses probability formally to demonstrate how teachers learn to make effective decisions by managing the inherent uncertainties and constraints of classroom teaching.

Palavras-chave : Bayesian reasoning; professional judgement; satisficing; uncertainty; teacher development; prediction error; robust priors.

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