MétaCan
Menu
Back to cohort
Record W3025046710 · doi:10.3389/frym.2020.00054

Understanding Your Brain to Help You Learn Better

2020· article· en· W3025046710 on OpenAlex
Jérémie Blanchette Sarrasin, Lorie‐Marlène Brault Foisy, Geneviève Allaire‐Duquette, Steve Masson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers for Young Minds · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversité du Québec à Montréal
FundersJacobs Foundation
KeywordsPsychologyCore KnowledgeCore (optical fiber)Computer scienceKnowledge management

Abstract

fetched live from OpenAlex

The past few years have been marked by a large number of discoveries about the learning brain. Those insights have the potential to support teachers in designing even better classroom environments to help you learn better. While understanding the brain can be helpful for teachers, this knowledge can also be beneficial for you as a student. For instance, it can encourage you to believe in your capacity to improve your own skills. Such beliefs make it more likely for you to make an effort and to make better use of supportive learning strategies [<xref ref-type="bibr" rid="B1">1</xref>]. In this article, we briefly present some core principles of the learning brain and suggest learning strategies inspired by neuroscience for you to try at school or at home.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.296
Teacher spread0.170 · 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