Computer‐assisted learning design for reflective practice supporting multiple learning styles for education and training in pre‐hosplital emergency care
Bibliographic record
Abstract
This article describes and discusses a unique project in the field of RP, CAL and Learning Styles. Reflective Practice (RP) is a method that can be used to assist the student to learn from experience using a carefully structured framework. This has been shown to result in new learning and improved practice. In PHEC the application of RP is seen as essential for clinical effectiveness and continuing professional development in paramedic practices. Evidence from Canada indicates that while PHEC work would be expected to attract convergers (as categorised by Kolb, 1984), in fact, the data did not support that expectation. No particular learning style was dominant. The teaching process for RP at the University of Hertfordshire normally uses the lecture‐discussion method between teacher/facilitators and students based largely on the presentation of scenarios and case studies with a wide‐ranging, but focused dialogue and discussion. The design problem discussed here was to provide computer–student interaction which offers the students additional opportunities to develop their own preferred styles as well as to achieve the same learning outcomes as the taught content.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".