e-Learning in medical education Guide 32 Part 2: Technology, management and design
Bibliographic record
Abstract
With e-learning now part of the medical education mainstream, both educational and practical technical and informatics skills have become an essential part of the medical teacher's portfolio. The Guide is intended to help teachers develop their skills in working in the new online educational environments, and to ensure that they appreciate the wider changes and developments that accompany this 'information revolution'. The Guide is divided into two parts, of which this is the second. The first part introduced the basic concepts of e-learning, e-teaching, and e-assessment, the day-to-day issues of e-learning, looking both at theoretical concepts and practical implementation issues. This second part covers topics such as practical knowledge of the forms of technology used in e-learning, the behaviours of teachers and learners in online environments and the design of e-learning content and activities. It also deals with broader concepts of the politics and psychology of e-learning, as well as many of its ethical, legal and economical dimensions, and it ends with a review of emerging forms and directions in e-learning in medical 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.041 |
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".