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Record W4225545444 · doi:10.26034/cortica.2022.1956

Mieux comprendre les mécanismes cérébraux d’apprentissage pour faciliter la mise en application des connaissances issues de la recherche et favoriser la réussite scolaire des élèves

2022· article· fr· W4225545444 on OpenAlexaff
Lorie‐Marlène Brault Foisy

Bibliographic record

VenueCortica · 2022
Typearticle
Languagefr
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Résumé Souvent désignée « décennie du cerveau », la décennie des années 90 a été marquée par l’accroissement des connaissances neuroscientifiques et l’avènement de nouvelles technologies d’imagerie cérébrale. Ces progrès en neurosciences ont progressivement mené la communauté de recherche à se questionner sur les retombées possibles des connaissances neuroscientifiques pour le domaine de l’éducation. Une nouvelle approche de recherche interdisciplinaire a ainsi émergé : la neuroéducation. Cette approche s’intéresse à des problématiques propres au milieu de l’éducation à l’aide d’un niveau d’analyse qui est celui du fonctionnement cérébral. La neuroéducation cherche donc à établir un pont entre le fonctionnement cérébral, les mécanismes liés à l’apprentissage et l’enseignement. De l’avis de plusieurs chercheurs et organisations internationales, une meilleure compréhension du cerveau pourrait en effet fournir des pistes intéressantes afin de mieux comprendre ce qui caractérise différents apprentissages sur le plan cérébral et, ultimement, guider le choix d’approches pédagogiques mieux adaptées à l’organisation et au fonctionnement du cerveau des élèves. D’une part, la neuroéducation permet en ce sens une compréhension plus fondamentale de différents apprentissages scolaires en s’intéressant aux changements qui s’opèrent durant l’apprentissage grâce à la neuroplasticité. D’autre part, plusieurs recherches ont mis en évidence que la neuroplasticité ne serait pas infinie et présenterait certaines limites. Elle serait en effet influencée par différentes contraintes, en particulier par la structure et l’organisation initiale du cerveau, c’est‑à‑dire l’architecture cérébrale préalable à l’apprentissage. Dans la lignée des recherches menées en psychologie cognitive, les résultats des recherches en neuroéducation fournissent déjà des points de repère intéressants pour guider le choix de certaines stratégies pédagogiques. Néanmoins, la mise en application des résultats de recherche à la salle de classe représente un défi considérable. À cet égard, le présent article vise à discuter de l’idée qu’une meilleure compréhension des mécanismes cérébraux d’apprentissage pourrait faciliter la mise en application des connaissances issues de la recherche et ainsi favoriser la réussite scolaire des élèves. Mots-clés : neuroéducation; contraintes cérébrales; neuroplasticité; enseignement Abstract Often referred to as the "decade of the brain," the decade of the 1990s was marked by the growth of neuroscience knowledge and the advent of new brain imaging technologies. These advances in neuroscience gradually led the research community to question the potential impact of neuroscientific knowledge on the field of education. A new interdisciplinary research approach has thus emerged: neuroeducation. This approach focuses on problems specific to the field of education using a level of analysis that is that of brain function. Neuroeducation therefore seeks to establish a bridge between brain functioning, the mechanisms related to learning and teaching. In the opinion of several researchers and international organizations, a better understanding of the brain could indeed provide interesting avenues to better understand what characterizes different learning processes at the brain level and, ultimately, guide the choice of pedagogical approaches that are better adapted to the organization and functioning of the students' brain. On the one hand, neuroeducation allows for a more fundamental understanding of various school learning processes by focusing on the changes that occur during learning through brain plasticity. On the other hand, several studies have shown that neuroplasticity is not infinite and has certain limits. It would indeed be influenced by different constraints, in particular by the initial structure and organization of the brain, i.e. the cerebral architecture prior to learning. In line with research in cognitive psychology, neuroeducational research findings already provide valuable insights to orient the choice of teaching strategies. Nevertheless, the application of research findings to the classroom presents a considerable challenge. In this respect, the present article aims to discuss the idea that a better understanding of the brain mechanisms of learning could facilitate the application of research findings and thus promote the academic success of students. Keywords: neuroeducation; brain constraints; neuroplasticity; teaching

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.138
GPT teacher head0.378
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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