Analysis of Student Perceptions of a Newly Developed Integrative System Course Model
Bibliographic record
Abstract
Background: During Spring 2021, we piloted a course model that integrated the immune system and HEENT (head, eyes, ears, nose, and throat) by concurrently presenting them in the context of clinical cases. Immune system topics (e.g., infection, cancer) were tied to their manifestations in the HEENT system, and concepts from both systems were consolidated in weekly case-based learning and small group discussion (CBL/SGD) sessions. Methods: To evaluate students' perceptions of the effectiveness of this model, we administered to the class a voluntary survey containing closed- and open-ended items; conducted a focus group of 10 students selected via convenience sampling; and employed a mixed approach to analyze the resulting data, including multiple qualitative methods. Results: Thirty-nine of 74 students completed the survey (53% response rate). In response to the item related to overall effectiveness of using CBL/SGD for system integration, nearly half (48.72%) of these students rated the overall effectiveness as average. Constant comparison analysis of the qualitative data revealed three major themes-student satisfaction with integration of immunology and HEENT, content and time involved in CBL/SGD, and suggestions for improvement-and classical content analysis revealed the relative importance of these themes. Participants held positive and negative perceptions, expressed concerns regarding CBL/SGD (e.g., its helpfulness, complexity), and made suggestions for improvement of integration. Conclusions: Using multiple methods allowed us to gain a deeper understanding of students' perceptions of the new course model, and we have taken actions to improve course quality in the future.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".