Math interest and self‐concept among latino/a students: Reciprocal influences across the transition to middle school
Bibliographic record
Abstract
INTRODUCTION: Psychological factors like math interest and self-concept typically decline between late childhood and early adolescence; both are key to math achievement. The present study examined the reciprocal interplay between math interest and self-concept across the transition into middle school, and whether associations are moderated by success attributions. METHODS: = 10.5 years at outset) from an agricultural community in California (USA) completed surveys at three time points, from the end of primary school to the first year of middle school. Surveys measured math self-concept and math interest, as well as attributions to success in math. Cross-lagged panel models examined possible bidirectional associations between math self-concept and math interest, and whether attributions of success moderated these association. RESULTS: Lower initial levels of math self-concept anticipated greater declines in math interest, an association that was buffered by attributions of math success. The smallest declines in math interest occurred among adolescents who had both the highest math self-concept and were most inclined to attribute success in math to internal factors like studying. These associations remained when potential confounding variables (e.g., school grades, conduct problems) were included. CONCLUSION: The results replicate, in an understudied sample of Latino/a youth, the oft-reported link from low math self-concept to declining interest in math. Unique to this study is evidence of the protection afforded by belief in the efficacy of studying. The findings offer important guidance for teachers and parents seeking to mobilize resources for underperforming students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".