Probabilistic professional judgement in teaching

Abstract

Subjective Bayesian reasoning offers a framework for understanding the development of probabilistic professional judgement in teaching. Drawing on Luhmann's systems theory, Simon's bounded rationality, and Shalem's work on professional knowledge, this paper demonstrates how Bayesian reasoning enables teachers to navigate three fundamental challenges: the operational separation between teaching and learning systems, cognitive limitations that necessitate satisficing solutions, and the systematic development of professional knowledge through academic and diagnostic classifications.

Through constructed scenarios, the paper demonstrates how novice teachers begin with fragile priors based on theoretical knowledge and personal experience, which undergo dramatic updates when confronted with classroom realities. As teachers gain experience, they develop more robust and refined priors—belief systems that can incorporate new evidence while maintaining stable overall patterns. This evolution reflects the development of sophisticated diagnostic classifications that guide professional decision-making.

The paper shows that subjective Bayesian reasoning provides a formal mechanism for modelling belief updating in professional judgement. While teachers may not engage in explicit probabilistic calculations, this paper argues that subjective Bayesian reasoning underlies the development of fast and frugal heuristics that become increasingly sophisticated with experience. By integrating Bayesian reasoning with established theories of professional knowledge development, a theoretical framework is offered that formally uses probability to demonstrate how teachers learn to make effective decisions under the inherent uncertainties and constraints of classroom teaching.