Education Theory Made Practical: Creating open educational resources via an apprenticeship model
Bibliographic record
Abstract
Abstract Introduction Clinical faculty may have limited knowledge of education theories and best practices in health professions education. Many faculty development programs focus on passive learning with limited application to practice. There is a need for more active engagement for early career educators. Methods We created an apprenticeship‐based electronic book series focused on translating education theories into practical applications for clinician educators. Chapters were authored by teams of two to four geographically separated early career educators, who were tasked with explaining an education theory and relating it to their educational practice. The chapters underwent internal peer review, followed by open peer review as a blog post and eventual publication. Usage data were collected, and surveys were sent to authors and end‐users. Results Six volumes (60 total chapters) have been created to date by 180 unique authors and 17 editors over a 6‐year period. There have been 65,571 total blog page views and 17,180 total book downloads across the five published volumes. Authors reported an increase in their perceived knowledge (pre 2.6 ± 1.7 vs. post 7.2 ± 1.1, mean difference 4.5/9.0, 95% confidence interval [CI] 4.0–5.0, p < 0.001) after writing their chapter. Authors also reported career benefits including authorship for academic advancement/promotion and developing an area of education theory expertise. End‐users also reported a mean increase in their perceived knowledge (pre 4.4 ± 2.5 vs. post 7.3 ± 1.4, mean difference 2.9/9.0, 95% CI 2.1–3.8, p < 0.001) after reading a chapter. Conclusion The Education Theory Made Practical electronic book series represents a proof of concept for an apprenticeship‐based model to teach education theory, while also creating scholarship and open access resources for the broader community.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".