Relations among competence beliefs, utility value, achievement goals, and effort in mathematics
Bibliographic record
Abstract
BACKGROUND: Research has shown that motivation is a key factor in the learning process as well as in school achievement. In essence, a number of researchers have highlighted the close link between motivation and achievement-related behaviours such as effort. AIMS: The present study aims to acquire more specific information concerning the relations between competence beliefs, utility value and achievement goals in mathematics among secondary school students, to further document the influence of social agents, and to better understand the relationships between these variables, as well as to effort. SAMPLE: Participants were 759 Grade 7 to Grade 11 students (389 males, 370 females). METHOD: Structural equation modelling techniques were used to test a model of achievement-related behaviours (effort) in mathematics based on support from social agents, competence beliefs, utility value and achievement goals. Several self-reported scales were administered. RESULTS: Results indicate that effort in mathematics is mainly explained by mastery goals and competence beliefs. As for the role of social agents, results demonstrated that the perception of parental support chiefly explained variables associated with the valuing of mathematics while teachers' support acted most on competence beliefs. CONCLUSIONS: Two main conclusions stem from our results. First, mastery goals have an important and significant impact on students' effort in the learning of mathematics. Second, the nature and the strength of the relationships between competence beliefs, utility value, achievement goals and effort are not significantly influenced by age and gender, at least 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".