Experiences with Audio Feedback in a Veterinary Curriculum
Bibliographic record
Abstract
On a national scale in the United Kingdom, student surveys have served to highlight areas within higher education that are not achieving high student satisfaction. Of particular concern to the veterinary and medical disciplines are the persistently poor levels of student satisfaction with academic feedback compared to students in other subjects. In this study we describe experiences with audio feedback trials in a veterinary curriculum. Students received audio feedback on either an in-course laboratory practical report or on an in-course multiple-choice test. Shortly after receiving their feedback, students were surveyed using an electronic questionnaire. In both courses, more students strongly agreed that audio feedback was helpful compared to either text-based (course A) or whole-class (course B) feedback. When asked to reflect on the helpfulness of various types of feedback they had received, audio feedback was rated less helpful than individual discussion with a member of staff (course A and course B), more helpful than peer discussion or automated feedback (course A and course B), and more helpful than written comments or whole-class review sessions (course B). From a faculty perspective, in course A, use of audio feedback was more efficient than handwritten feedback. In course B, the additional time commitment required was approximately 5 hours. Major themes in the qualitative data included the personal and individual nature of the feedback, quantity of feedback, improvement in students' insight into the process of marking, and the capacity of audio feedback to encourage and motivate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".