Veterinary Students’ Use of Learning Objectives
Bibliographic record
Abstract
Learning objectives (LO) are the foundation of a competency-based curriculum, but no studies assess how students use LO for exam preparation and/or their general attitudes toward LO. Therefore, the objectives were to evaluate how much veterinary students use LO to study, assess student attitudes toward simple and expanded LO, and determine if LO type impacts grade performance. An alternative-treatment design with pre-test and nonrandom groups was used. Veterinary students in the Endocrine Systems course in the 2019 spring ( n = 89) and fall ( n = 86) semesters were invited to participate and provided with simple and expanded LO, respectively. After an examination, participants completed an online survey before and after receiving their grade. Overall, 114 students (65%) responded. The percentage of students using simple versus expanded LO was not statistically different (χ2 = 1.874, df = 1, p = .171). Fifty-five students did not use LO; the majority (76.4%) preferred other study methods. Independent samples t-tests found no significant differences in student perceptions of helpfulness ( t(30) = −1.118, p = .272), format ( t(29) = 0.813, p = .423), or relevance ( t(30) = 0.326, p = .747) between simple and expanded LO. Students agreed that LO were helpful ( M = 3.33) and well formatted ( M = 3.42) and that the provided information was relevant and detailed ( M = 3.36). An ANOVA tested whether exam grade differed between students using simple versus expanded LO and for students who did not use LO; no significant differences were found ( F(2,78) = .087, p = .917). In conclusion, students did not prefer more detailed LO and LO use did not impact grade performance.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".