Relations of multivariate goal profiles to motivation, epistemic beliefs and achievement
Bibliographic record
Abstract
We examined whether undergraduates’ achievement goal orientations could be represented as profiles and whether profiles were linked to self-reported motivation, epistemic beliefs and academic achievement. Data collected during an undergraduate course were analyzed using a clustering technique. Using the 2 × 2 goal model (Elliot & McGregor, 2001 ), we identified five achievement goal profiles. Our findings suggest the interaction of goal orientations supports varying interpretations of students’ motivation and learning beliefs. Although no statistically significant differences in achievement were found across clusters, a High-Approach-Low-Avoidance cluster displayed an adaptive profile that was most positive towards learning and self but least anxious about exams. In contrast, a Performance-Avoidance-Dominant cluster demonstrated a maladaptive pattern of lowest self-efficacy and task value, and higher anxiety. Further, High-Approach-Low-Avoidance and Low-Performance-Avoidance clusters recognized that knowledge is not simple and authority could be questioned, compared to the other groups.
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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 | 0.010 |
| 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 | 0.000 |
| 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".