THE CAUSAL RELATIONSHIPS BETWEEN ATTRIBUTION STYLES, MATHEMATICS SELF-EFFICACY BELIEFS, GENDER DIFFERENCES, GOAL SETTING, AND MATH ACHIEVEMENT OF SCHOOL CHILDREN
Bibliographic record
Abstract
This study was conducted to investigate the causal relationships between attribution styles, mathematics self-efficacy beliefs, gender differences, goal setting, and math achievement of school children. The subjects were 99 seventh-grade students (56 male and 43 female), from public schools in Sooke, Canada, who were selected randomly to participate in this study. A model was tested, using AMOS software. The scales used consisted of modified Stipek s (1993), Attribution for Performance in Math, Self-Efficacy Scale, the Foundation Skills Assessment tests and Lock and Bryan (1968) modified Students Grade Goals Rating Scale. The results showed that the model was statistically fit and that student s math selfefficacy was influenced by his/her attribution. A significant relationship was found between self-efficacy and internal attribution. Also, the path between math selfefficacy and goal setting was significant, implying that self-efficacy plays a key role in students goal setting. In addition, the students goal setting was a predictor of the math achievement. Actually, those students who set higher goals for themselves in mathematics get better grades in math. The effect of self-efficacy on math achievement was indirect, through goal setting. The significant path between the attribution and math achievement shows that the explanatory styles influence math achievement. In other words, those students who attribute the causes of their success in math to internal factors receive higher grades, and those students who attribute the causes of their failure in math to internal factors get lower grades in mathematics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".