Applying learning design principles in the development of training materials for paramedic instructors
Bibliographic record
Abstract
In Saskatchewan, the level of medical training determines the certification of the paramedic; for example, Primary Care or Advanced Care. There is no designation for instructors, although as the governing body, the Saskatchewan College of Paramedics (SCoP) is considering moving in this direction. Currently, SCoP does not require paramedic instructors to complete training in adult learning theories or instructional design as a pre-requisite for delivering paramedic training courses. Therefore, there is a need to develop instructional materials for paramedic instructor candidates, vis-à-vis adult learning theories, learning design principles, and instructor competencies. Examination of these concepts is critical to developing a strong foundation for effective teaching and facilitation, whether face to face or online. This final report describes the plan and process for design and development of three self-paced, online module prototypes that will be used to provide “proof of concept” for a paramedic instructor certification course. The three modules cover the following three topics; adult learning theories, writing learning objectives, and designing assessments. The design and development of these online module prototypes are informed by research and include a thorough investigation of relevant literature sources to ensure the modules employ appropriate instructional and assessment strategies.
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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 | 0.023 | 0.034 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".