Computational Neuroscience and Education Science


Artificial Intelligence Devoted to Education: We want to explore to what extent approaches or methods from cognitive neuroscience, linked to machine learning and knowledge representation, could help to better formalize human learning as studied in educational sciences. In other words: we are taking advantage of our better understanding of how our brains work to help us better understand how our children learn. The focus here is on learning computational thinking, i.e., what to share in terms of the skills needed to master the digital world, not just consume or endure it, considering specific learning tasks modeling.


General presentation

Preliminary outcomes

Who participates ?

Contact

French summary

  General presentation

The three operational objectives of the AIDE project

  • At the theoretical level, use the machine learning formalism as a model of the learner, as it can be done in computational neuroscience on another scale, e.g. consider reinforcement learning framework to encounter for the human learning behavior.
  • At the experimental level, use machine learning tools to obtain more formalized, more reliable, and more automated measures of variables related to a learning situation, using real life objects (e.g., unplug activity, manipulation of tangible connected objects) .
  • At the pedagogical level, help the learner to understand in his own process of learning to learn, which is a matter of mechanical processes of what is creative and making these two aspects explicit.

In other words, before using artificial intelligence to, for example, improve human learning, let’s try to use it to better understand how a human learns.

  This Exploratory action (AEx) is a four year project started middle 2020 until 2024.

 Preliminary outcomes

We introduced the idea of a symbolic description of a complex human learning task, in order to contribute to better understand how we learn [mercier:hal-03360307], in the very precise framework of a task, named \#CreaCube, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity. We also proposed to map an ontology onto a SPA-based architecture with a preliminary partial implementation into spiking neural networks [mercier:hal-03360307], in order to provide an effective link between symbolic presentation of information and biologically plausible numerical implementation, including regarding representation of belief revisiting the possibility theory [vallaeys:hal-03338721]. We furthermore proposed to make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology [mercier:hal-03327706]. This work is embedded in strong collaboration with education science collaborators [romero:hal-02957270] working on computational thinking initiation and computer science tools in education with a multi-disciplinary vision of cognitive function modeling [denet:hal-03382314].

 Who participates ?

  • Axel Palaude (PhD Student)
  • Chloé Mercier (PhD Student)
  • Didier Roy (Researcher)
  • Frédéric Alexandre (Researcher)
  • Hélène Sauzeon (Researcher)
  • Margarida Romero (Researcher)
  • Thierry Viéville (Researcher)

 with the advises and help of  Éric Pascual (Engineer), Dorian Mazauric (Researcher),
Gabriel Doriath-Döhler (former Intern student), Gérard Giraudon (Researcher)
Hélène Sauzéon (Researcher),  Sabrina Barnabé (Craftwoman), and thanks to former collaborators Ali Issaoui (former Intern Student),Bhargav teja Nallapu (former PhD student), Divya Menon (former MSc Intern student), [É]Lisa Roux (former PostDoc student), Jia Fu (former Intern student),
Lola Denet (former MSc Intern student), Thalita Firmo-Drumond  (former PhD student), Théophane Vallaeys (former Intern student), Younes Jallouf (former Intern student).

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