Examining the Motivational Climate and Student Effort in Professional Competency Courses: Suggestions for Improvement
Bibliographic record
Abstract
The attainment of professional competencies leads to essential skills for successful and employable veterinarians. However, the inclusion of professional competencies in veterinary curricula is often underdeveloped, and it is sometimes less appreciated by students than the science/technical skill curricula. The aim of this study was to better understand students' motivation within professional competency courses (PC courses) by (a) comparing students' motivational perceptions in PC courses to those in science/technical skill courses (ST courses), (b) determining the extent to which students' motivational perceptions predict their course effort, and (c) identifying teaching strategies that could be used to improve PC courses. Participants included students from eight courses enrolled in their first or second year of a veterinary college at a large land-grant university in the United States. A partially mixed concurrent dominant status research design was used to collect quantitative and qualitative data. Students completed closed- and open-ended survey items regarding their effort and the motivational climate in their courses. Compared to ST courses, students put forth less effort in PC courses; rated PC courses lower on empowerment, usefulness, and interest; and had higher success expectancies in PC courses. Although students' perceptions of empowerment, usefulness, interest, and caring were significantly correlated with their effort, interest was the most significant predictor of effort in both PC and ST courses. Based on students' responses to the open-ended questions, specific motivational strategies are recommended to increase students' effort in PC courses, such as intentionally implementing strategies to increase students' interest and perceptions of usefulness and empowerment.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".