MétaCan
Menu
Back to cohort
Record W3082136167 · doi:10.3389/frym.2020.00110

It Is Complicated: Learning and Teaching Is Not About “Learning Styles”

2020· article· en· W3082136167 on OpenAlex
Breanna Lawrence, Burcu Yaman Ntelioglou, Todd Milford

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 institutionsBrandon UniversityUniversity of Victoria
FundersJacobs Foundation
KeywordsLearning stylesStyle (visual arts)Mathematics educationExperiential learningActive learning (machine learning)Matching (statistics)Learning theorySimple (philosophy)MythologyTeaching methodPsychologyLimit (mathematics)Computer sciencePedagogyArtificial intelligenceEpistemologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

Learning styles is perhaps one of the most widespread and believed myths in education. The idea is based on the claim that all students can be classified according to their particular learning style, and that they learn best when teachers match instruction to the preferred style of the student. This popular theory has been proven false by many learning scientists. Learning styles theory reduces sophisticated and complex processes like teaching and learning into overly simple categories and labels students in ways that can limit their potential. Studies performed by scientists who study the brain and education have found that learning and teaching are much more complicated than simply matching teaching to a student’s learning style.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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