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Record W4280535981 · doi:10.1111/bjep.12510

The role of the classroom learning environment in students’ mathematics anxiety: A scoping review

2022· review· en· W4280535981 on OpenAlexafffund
Gabrielle O’Hara, Heather Kennedy, Michael Naoufal, Tina Montreuil

Bibliographic record

VenueBritish Journal of Educational Psychology · 2022
Typereview
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsMcGill University Health CentreMcGill University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsPsychologyAnxietyContext (archaeology)Inclusion (mineral)Mathematics educationDemographicsVulnerability (computing)Intervention (counseling)Sample (material)Developmental psychologyLearning environmentMathematical anxietySocial psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Math anxiety is a common experience that interferes with learning and achievement in mathematics. Considering that mathematics learning mostly takes place within the classroom, it is critical to examine how math anxiety develops in this context. AIMS: The purpose of the current scoping review was to identify classroom-learning environment factors associated with math anxiety in elementary and high school students. SAMPLE(S): Out of an initial sample of 3011 studies, 28 were eligible for inclusion. METHODS: Data on author(s), publication year, and study location; sample demographics; classroom variables; intervention details (if applicable); measures; and key results were extracted from articles. RESULTS: Numerous protective and vulnerability factors were identified. CONCLUSIONS: Directions for future research and methodological implications were explored.

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)0.007
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.001
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 head0.052
GPT teacher head0.433
Teacher spread0.381 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2022
Admission routes2
Has abstractyes

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