Achievement Goal Orientation and Curricular Engagement in Veterinary Students
Bibliographic record
Abstract
Achievement goal orientation (AGO) defines student motivation and whether they are driven by learning and competence (mastery orientation) or performance markers, such as grades (performance orientation). Competency-based veterinary education aligns with a mastery orientation, particularly when students are assessed on the basis of achieving competence, rather than using grades. This study uses a mixed-methods approach to report the AGO of veterinary students and evaluate their responses to educational challenges, describe how they maintain motivation, and explore their perceptions of grades using AGO as a theoretical framework. Most students are more strongly mastery oriented but also typically have moderate to strong performance goal orientation. Focus group analysis shows evidence of both orientations in students' behaviors, regardless of their predominant orientation, and these behaviors are, at times, conflicting. Curricular overload is viewed as a significant educational challenge, exacerbating performance avoidance behaviors (e.g., procrastination). Students profess to enjoy educational challenge but are adept at constructing narratives around why some challenges are not considered fair or legitimate, which may suggest false mastery mind-set. They also tend to state preferences for pass/fail grading but have complex feelings about grades, and some desire objective feedback on performance relative to peers. In the absence of objective feedback, students leverage a variety of methods (both mastery- and performance-oriented) to maintain motivation and achieve comparisons with peers. Withdrawal of objective measures such as grades, grade point average, and class rank would need to be carefully coupled with sufficient feedback and support for students who may rely on these as motivators.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".