Climbing the Integration Ladder: A Case Study on an Interdisciplinary and Case-Based Approach to Teaching General Pathology, Parasitology and Microbiology in the Veterinary Curriculum
Bibliographic record
Abstract
The School of Veterinary Medicine, University College Dublin, Ireland, restructured the teaching of general pathology, parasitology, and microbiology in third year in 2018 as part of the development of an outcome-based curriculum. A new integrated teaching module was created, called Veterinary Pathobiology, which encompassed the three paraclinical subjects, worth 20 ECTS credits. Subject integration was driven and supported by case-based learning (CBL) activities, and practical classes, which were aimed at facilitating the understanding of basic disease processes, infectious agents, and the application of diagnostic tests. The disciplines maintained their identities within lectures which were aligned by content. The restructuring led to a reduction of contact hours by 20% and of assessment time by 40%. The examinations included integrated questions with an emphasis on the material students had covered in their CBL. Despite positive outcomes, which included equivalent examination scores and positive written feedback by students on teaching and learning, understanding, assessment, relevance, CBL, group work, and generic skills, the average scores for overall student satisfaction dropped dramatically in the second academic year of implementation. This followed the introduction of new regulations by the University relating to student progression, which was capped at "carrying" 10 ECTS credits, thus preventing students that failed the new module from progressing. Other criticisms of the new module by students included too little communication on the changes implemented in its first iteration and a workload perceived to be too heavy. Further restructuring is therefore necessary. This study highlights the process/pitfalls of integration/curricular innovation.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".