MétaCan
Menu
Back to cohort
Record W4395000894 · doi:10.3389/fpsyg.2024.1333012

The effect of student-perceived teacher support on math anxiety: chain mediation of teacher–student relationship and math self-efficacy

2024· article· en· W4395000894 on OpenAlexaff
Chao Wang, Qing Xu, Wei-qun Fei

Bibliographic record

VenueFrontiers in Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsMcMaster University
FundersMinistry of Education of the People's Republic of China
KeywordsPsychologyMathematical anxietyMediationAnxietyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

Introduction: This study investigates the mechanisms linking students' perceived teacher support with math anxiety, focusing on the mediating roles of the teacher-student relationship and mathematics self-efficacy. Methods: The research was conducted with 401 fifth-grade students in China, utilizing scales for Students' Perceived Teacher Support, Teacher-Student Relationship, Math Self-Efficacy, and Math Anxiety. Results: Findings revealed that student-perceived math teacher support, teacher-student relationship, and math self-efficacy were all significantly negatively correlated with math anxiety. It was notably found that student-perceived math teacher support influenced math anxiety through the chain mediation of teacher-student relationship and math self-efficacy. Additionally, the effect of students' perceived emotional support from math teachers on math anxiety, mediated by teacher-student relationship intimacy, was significant only among male students. Discussion: These results underscore the importance of fostering positive teacher-student interactions and enhancing self-efficacy to reduce math anxiety among primary school students. The gender-specific findings regarding emotional support and relationship intimacy highlight the need for tailored strategies in addressing math anxiety.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.017
GPT teacher head0.367
Teacher spread0.350 · 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.

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

Citations25
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PsychologySame topicEducation, Achievement, and GiftednessFrench-language works237,207