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A Hierarchical Bayesian Model of Adaptive Teaching.
Chen, Alicia M; Palacci, Andrew; Vélez, Natalia; Hawkins, Robert D; Gershman, Samuel J.
Afiliación
  • Chen AM; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
  • Palacci A; Department of Psychology, Harvard University.
  • Vélez N; Department of Psychology, Princeton University.
  • Hawkins RD; Department of Psychology, University of Wisconsin-Madison.
  • Gershman SJ; Department of Psychology, Harvard University.
Cogn Sci ; 48(7): e13477, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38980989
ABSTRACT
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enseñanza / Teorema de Bayes / Aprendizaje Límite: Adult / Female / Humans / Male Idioma: En Revista: Cogn Sci Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enseñanza / Teorema de Bayes / Aprendizaje Límite: Adult / Female / Humans / Male Idioma: En Revista: Cogn Sci Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos