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Research in Subject-matter
Teaching and Learning
ristalResearch in Subject-matter
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RISTAL. Research in Subject-matter Teaching and Learning 4 (2021), pp. 88–108

Modeling Teachers’ Diagnostic Judgments by Bayesian Reasoning and Approximative Heuristics

Special Issue
Special Issue

Special Issue edited by Timo Leuders & Katharina Loibl

Katharina Loibl

Timo Leuders

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DOI: https://doi.org/10.23770/RT1844 | Date: 05/18/2021

The diagnostic judgments teachers make can be regarded as inferences from manifest observable evidence on students’ behavior (e.g., responses to a task) to their latent traits (e.g., misconceptions). The judgment process can be modeled by Bayesian reasoning. We use this framework to analyze the situation of teachers’ diagnostic judgments on students’ potential misconceptions based on students’ responses. Humans typically deviate from normative Bayesian reasoning and apply heuristic strategies, often by only partially processing the available information (e.g., neglecting base rates). From the perspective of ecological rationality, such heuristics possibly constitute viable cognitive strategies for assessing student errors. We investigate the adequacy of a cognitively plausible heuristic strategy, which amounts to approximating the average probability information on prior hypotheses (base rates of student misconceptions) and evidence (student errors). With a computational simulation, we compare this strategy to optimal Bayesian reasoning and to information-neglecting strategies. We interpret the resulting accuracy within the ecology of informal student assessment.

diagnostic judgment, Bayesian reasoning, heuristic, computational simulation

THEO WEB Zeitschrift für Religionspädagogik
Academic Journal of Religious Education

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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