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TOPS

Financiado

Machine-Assisted Teaching for Open-Ended Problem Solving: Foundations and Applic...

Machine-Assisted Teaching for Open-Ended Problem Solving: Foundations and Applications Computational thinking and problem solving skills are essential for everyone in the 21st century, both for students to excel in STEM+Computing fields and for adults to thrive in the digital economy. Consequently, educators are put... ver más

31/03/2027

MPG

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-02-14

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 3 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

MPG

1.50M€ | Lider

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Duración del proyecto: 61 meses Fecha Inicio: 2022-02-14
Fecha Fin: 2027-03-31

Líder del proyecto

MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Computational thinking and problem solving skills are essential for everyone in the 21st century, both for students to excel in STEM+Computing fields and for adults to thrive in the digital economy. Consequently, educators are putting increasing emphasis on pedagogical tasks in open-ended domains such as programming, conceptual puzzles, and virtual reality environments. When learning to solve such open-ended tasks by themselves, people often struggle. The difficulties are embodied in the very nature of tasks being open-ended: (a) underspecified (multiple solutions of variable quality), (b) conceptual (no well-defined procedure), (c) sequential (series of interdependent steps needed), and (d) exploratory (multiple pathways to reach a solution). These struggling learners can benefit from individualized assistance, for instance, by receiving personalized curriculum across tasks or feedback within a task. Unfortunately, human tutoring resources are scarce, and receiving individualized human-assistance is rather a privilege. Technology empowered by artificial intelligence has the potential to tackle this scarcity challenge by providing scalable and automated machine-assisted teaching. However, the state-of-the-art technology is limited: it is designed for well-defined procedural learning, but not for open-ended conceptual problem solving. The TOPS project will develop next-generation technology for machine-assisted teaching in open-ended domains. We will design novel algorithms for assisting the learner by bridging reinforcement learning, imitation learning, cognitive science, and symbolic reasoning. Our theoretical foundations will be based on a computational framework that models the learner as a reinforcement learning agent who gains mastery with the assistance of an automated teacher. In addition to providing solid foundations, we will demonstrate the performance of our techniques in a wide range of pedagogical applications.

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