Learning design in healthcare education
Bibliographic record
Abstract
Emerging from ongoing work into educational modelling languages, learning design principles and the IMS Learning Design framework provide formal ways to annotate and record educational activities. Once educational activities have been encoded they can be played, replayed, adopted, shared, and analysed, thereby reifying much that is otherwise lost in face-to-face teaching. The use of learning design tools, including the free and open source LAMS system (www.lamsfoundation.org), allow practitioners to experiment with learning design approaches in their own teaching, both in terms of creating and encoding their own designs and playing, adapting and analysing designs from other teachers either from within or outside a particular field or subject area. This paper reviews the key issues associated with designing for learning in the context of healthcare education, some of the themes and approaches already in development or use, and the implications of this approach on the practice and theory of healthcare education.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".