Using Focus Groups to Engage Veterinary Students in Course Redesign and Development
Bibliographic record
Abstract
Students' perceptions about the quality of teaching have been shown to influence their approaches to learning and studying. The literature suggests that understanding student perceptions is critical to making informed decisions about curriculum development so that courses meet objectives, enhance engagement, and, ultimately, improve learning. However, the assessment of students' perceptions of their courses and the quality of teaching is frequently limited to an end-of-term course evaluation survey. While these course evaluations may be useful in providing a summative assessment, they do not typically provide insight into the reasons and influences that underlie student ratings. Achieving this type of understanding can be accomplished through qualitative methodology, which is a process of investigation used to reveal the depth, complexity, and nuances of perceptions and experiences. In the current article, we report the use of focus groups as a method of gaining in-depth understanding of student perceptions for course redesign. We present the redesign of the Art of Veterinary Medicine II course, a second-year core offering within the Doctor of Veterinary Medicine curriculum at the Ontario Veterinary College in Guelph, Canada. A series of student focus groups were held to gain greater insight into student perceptions of the course objectives, format, and content. Findings were then considered in the redevelopment of the course to better engage students and their various learning styles. Summative course evaluations as well as informal feedback before and after the focus groups indicate a notable improvement in student experiences and perceptions of the course format and content following the focus-group informed course redesign.
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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 | 0.011 | 0.004 |
| 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) | 0.000 | 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".