Math Anxiety, Achievement and Perceptions of Same-Ethnic Peers in Math Class
Bibliographic record
Abstract
Using multiple linear regression analysis, this research explores racially and ethnically diverse students’ feelings of math anxiety, how these beliefs shape their achievement in the subject, and whether students’ math anxiety and performance in mathematics vary based on students’ gender, race/ethnicity, and math level. Moreover, this study investigated the potential protective functions of perceiving a high proportion of same-ethnic peers in math class for buffering against the detrimental effects of high math anxiety on achievement. Results showed that when African American students reported a high level of math anxiety, their math grades were lower when they also perceived there to be a high proportion of same-ethnic peers in their math course compared to White students with similar levels of math anxiety and perceptions of same-ethnic peers. These results suggest that the effects of classroom same-ethnic representation for students’ academic outcomes are more nuanced than labeling it as a “protective” factor. Other contextual factors may influence the relationship between math anxiety, perceived same-ethnic representation in math class, and achievement and should be explored in future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".