Facilitating Capability: iSAP – A Novel Pedagogical Intervention
Bibliographic record
Abstract
This study explores the educational impact, as perceived by students, of an innovative, online context-rich case-based learning assessment and reflection framework known as integrating Science and Practice (iSAP). The study draws on a cross-sectional study conducted from 2018 to 2020 for students in units of study where an iSAP case was used as one assessment method. The voluntary survey was administered via the university’s learning management system at the end of each academic year (Semester 2). The 19 self-rating questions were presented as counts and percentages. The difference in item responses between undergraduate and postgraduate students was explored using chi-squared tests. We observed that the majority of students perceived iSAP as a well-rounded framework that helped them apply knowledge and skills to real-life situations, improved their understanding of study material and helped them develop evidence-based reasoning, critical thinking and reflective analysis skills. We also observed that postgraduate students, compared to their undergraduate counterparts, were more likely to agree with statements related to the authenticity of the iSAP case as well as the benefits of iSAP to individual learning, further engagement in research to extend their knowledge and development of their evidence-based reasoning skills and critical thinking skills. We conclude that iSAP is an innovative case-based learning and assessment approach to facilitate preparation for professional and clinical capability. Further research is required to determine the impact of student professional maturity and how this may impact upon students’ perception and adaptation of the reflective component of the iSAP framework.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".