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Poster De Conférence Année : 2023

Towards computational learning science: modeling creative problem solving in child-robot interaction through behavioral learning analytics

Une approche computationnelle des sciences de l'éducation : modélisation de la résolution créative de problèmes dans l'interaction enfant-robot par le biais de traces d'apprentissage de type comportamentale

Résumé

In computational neuroscience and bioinspired artificial intelligence (AI), different studies aim at understanding the mechanisms of perception such as shape recognition and sensori-motor coordination such as grasping or pinching. A step further, as in the Mnemosyne research program, cognitive mechanisms are analyzed to better understand brain circuits responsible for reasoning and problem-solving by a biological or algorithmic agent. The ANR CreaMaker and AIDE research programs seek to improve understanding of creative problem solving via behavioral analysis of the relationship between brain processes and problem-solving activities. The CPS behavior (intentions + CPS regulation) is analyzed using learning analytics created automatically as well as human analysis based on coding schemes. #CreaCube is a large dataset of creative problem-solving challenges (n>1300 experiments). Each video is coded in accordance with a behavioral learning analytics coding schema. #CreaComp is a computational learning sciences research program dedicated to the improvement of the study of creative problem solving.
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hal-04124474 , version 1 (10-06-2023)

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Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales

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  • HAL Id : hal-04124474 , version 1

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Margarida Romero, Frédéric Alexandre. Towards computational learning science: modeling creative problem solving in child-robot interaction through behavioral learning analytics. ISLS 2023 - Annual Meeting of the International Society of the Learning Sciences, Jun 2023, Montréal, Canada. ⟨hal-04124474⟩
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