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Record W2027927149 · doi:10.1016/j.paid.2015.02.006

Why children differ in motivation to learn: Insights from over 13,000 twins from 6 countries

2015· article· en· W2027927149 on OpenAlexaff
Yulia Kovas, Gabrielle Garon‐Carrier, Michel Boivin, Stephen A. Petrill, Robert Plomin, Sergey Malykh, Frank M. Spinath, Kou Murayama, Juko Ando, O. Yu. Bogdanova, Mara Brendgen, Ginette Dionne, Nadine Forget‐Dubois, Eduard V. Galajinsky, Juliana Gottschling, Frédéric Guay, Jean‐Pascal Lemelin, Jessica A. R. Logan, Shinji Yamagata, Chizuru Shikishima, Birgit Spinath, Lee A. Thompson, Tatiana Tikhomirova, Maria Grazia Tosto, Richard E. Tremblay, Frank Vitaro

Bibliographic record

VenuePersonality and Individual Differences · 2015
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversité de MontréalUniversité de SherbrookeUniversité du Québec à MontréalUniversité Laval
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMedical Research CouncilTomsk State University
KeywordsPsychologyDevelopmental psychologySimilarity (geometry)Competence (human resources)PerceptionCognitionConsistency (knowledge bases)Twin studySocial psychologyHeritability

Abstract

fetched live from OpenAlex

Little is known about why people differ in their levels of academic motivation. This study explored the etiology of individual differences in enjoyment and self-perceived ability for several school subjects in nearly 13,000 twins aged 9-16 from 6 countries. The results showed a striking consistency across ages, school subjects, and cultures. Contrary to common belief, enjoyment of learning and children's perceptions of their competence were no less heritable than cognitive ability. Genetic factors explained approximately 40% of the variance and all of the observed twins' similarity in academic motivation. Shared environmental factors, such as home or classroom, did not contribute to the twin's similarity in academic motivation. Environmental influences stemmed entirely from individual specific experiences.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.317
Teacher spread0.235 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations81
Published2015
Admission routes1
Has abstractyes

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