The effect of student-perceived teacher support on math anxiety: chain mediation of teacher–student relationship and math self-efficacy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".