Crowdsourced Curriculum Development for Online Medical Education
Bibliographic record
Abstract
In recent years online educational content, efforts at quality appraisal, and integration of online material into institutional teaching initiatives have increased. However, medical education has yet to develop large-scale online learning centers. Crowd-sourced curriculum development may expedite the realization of this potential while providing opportunities for innovation and scholarship. This article describes the current landscape, best practices, and future directions for crowdsourced curriculum development using Kern's framework for curriculum development and the example topic of core content in emergency medicine. A scoping review of online educational content was performed by a panel of subject area experts for each step in Kern's framework. Best practices and recommendations for future development for each step were established by the same panel using a modified nominal group consensus process. The most prevalent curriculum design steps were (1) educational content and (2) needs assessments. Identified areas of potential innovation within these steps included targeting gaps in specific content areas and developing underrepresented instructional methods. Steps in curriculum development without significant representation included (1) articulation of goals and objectives and (2) tools for curricular evaluation. By leveraging the power of the community, crowd-sourced curriculum development offers a mechanism to diffuse the burden associated with creating comprehensive online learning centers. There is fertile ground for innovation and scholarship in each step along the continuum of curriculum development. Realization of this paradigm's full potential will require individual developers to strongly consider how their contributions will align with the work of others.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".