Exploring student perceptions of course resources in an undergraduate animal physiology course
Bibliographic record
Abstract
There has been a significant shift in student learning needs and strategies during and after the COVID-19 pandemic (Becker et al., 2022). In this context, traditional resources and approaches may not be suffcient to satisfy the new needs of students. This study, set in the post-pandemic landscape, focuses on an undergraduate physiology course where students struggled with learning despite having access to the same resources as before the pandemic. Our main objective was to understand students’ experiences with the existing course resources in the post-pandemic era and gather their suggestions for improvements. Using focus groups, we garnered insights into the student experience and perceptions in a second-year animal physiology course. The analysis of focus group interview data indicated that students faced various challenges utilizing the existing resources for understanding concepts and preparing for exams. The focus group also provided recommendations on how the resources related to the lecture content, assessment, and lab operation can be improved to enhance student learning experiences. Results from this study will be used to inform the development of innovative teaching resources to satisfy student demands in undergraduate-level physiology learning. University of Toronto Faculty of Arts and Science Pedagogical Research Grant. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".