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COMPTEACH

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Computational mechanisms of teacher-pupil knowledge transfer

Teaching is one of the most complex and efficient forms for social learning, as it largely mitigates the costs of individual learning through the capitalization of others’ experiences. However, while recent efforts have shed consi... ver más

31/01/2026

ENS

Presupuesto desconocido

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ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-02-01

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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Duración del proyecto: 23 meses Fecha Inicio: 2024-02-01
Fecha Fin: 2026-01-31

Líder del proyecto

ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Teaching is one of the most complex and efficient forms for social learning, as it largely mitigates the costs of individual learning through the capitalization of others’ experiences. However, while recent efforts have shed considerable light into how human learning is computationally modeled, very little is known about how human teaching can be computationally implemented in the context of goal-directed behaviours. COMPTEACH is an innovative research program conceived to bridge this gap by combining cutting-edge methods in cognitive science, experimental and social psychology, with state-of-the-art techniques from Reinforcement Learning (RL) and Natural Language Processing (NLP). It aims at understanding the computational makeup of pedagogical knowledge transfer between experienced learners (teachers) and novel naive learners (pupils). COMPTEACH will first identify actors’ choice strategies (as indexed by model parameters) and evaluate the extent to which pedagogical texts crafted by teachers and addressed to pupils lead to meaningful parameter correlations between the two. Secondly, the action will implementNLP tools to identify how the teachers' own computational strategies are encoded in the semantic and syntactic structures of pedagogical texts. Finally, the project will study how the teachers’ own learning process and metacognitive features affect knowledge transfer. By tackling teaching from a computational perspective without losing sight of its socio-cognitive underpinnings, COMPTEACH will produce a novel quantitative understanding of experience transfer, and establish novel bridges between teaching, cognitive science, social psychology and computational modeling. Future directions of this project will involve studying knowledge transfer in individuals afflicted by lasting underlying health conditions, and the development of ecological experiments to approach teacher-student and doctor-patient real-world interactions.

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