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Record W3133822464 · doi:10.20849/jed.v5i1.866

Students’ Math Self-Concept, Math Anxiety, and Math Achievement: The Moderating Role of Teacher Support

2021· article· en· W3133822464 on OpenAlexaffabout
Satoshi ODA, Chiaki Konishi, Takashi Oba, Tracy K. Y. Wong, Xiaoxue Kong, Chloé St. Onge-Shank

Bibliographic record

VenueJournal of Education and Development · 2021
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsMcMaster UniversityMcGill University
Fundersnot available
KeywordsMathematical anxietyAnxietyModerationPsychologyAssociation (psychology)Developmental psychologyEmotional supportMathematics educationSocial psychologySocial support

Abstract

fetched live from OpenAlex

This study explored the moderating roles of teacher instrumental and emotional support on the association between students’ math anxiety/math self-concept and math achievement. Participants included 21,544 Canadian students aged 15 years (10,943 girls) who participated in the 2012 Program for International Student Assessment. Results indicated that instrument support and emotional support were positively associated with math achievement. A significant moderation effect was evident between instrumental support and math anxiety; higher levels of instrumental support were associated with higher math achievement at low levels of math anxiety. Emotional support did not interact with math anxiety or math self-concept. The present findings highlight the importance to consider not only individual factors (i.e., math anxiety and math self-concept) but also the role of teacher support in supporting math achievement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.326
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes2
Has abstractyes

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