Universal Beliefs and Specific Practices: Students’ Math Self-Efficacy and Related Factors in the United States and China
Bibliographic record
Abstract
<p class="apa">This study intends to compare and contrast student and school factors that are associated with students’ mathematics self-efficacy in the United States and China. Using hierarchical linear regressions to analyze the Programme for International Student Assessment (PISA) 2012 data, this study compares math self-efficacy, achievement, and variables such as math teacher support and socioeconomic status (SES) between 15-year-old students in the U.S. and in Shanghai, China. The findings suggest that on average, students from Shanghai showed higher math self-efficacy and better achievement than those of American students. However, at the student level, similar positive relationships between math teacher support and math self-efficacy and between SES and math self-efficacy were found in both locations. That is, in the U.S. and Shanghai, an increase in math teacher support predicts an increase in math self-efficacy, also higher SES is significantly associated with higher math self-efficacy. In addition, at the school level, the smaller difference in American students’ math self-efficacy between higher SES school and lower SES school indicates that the U.S. is more equitable between schools than Shanghai, China in terms of students’ math self-efficacy. Implications from this study indicate that improving teacher support in math class and narrowing the gap in students’ self-efficacy related to school-level SES is a significant issue for the U.S. and Shanghai, China respectively.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".