Twelve tips for improving the effectiveness of web-based multimedia instruction for clinical learners
Bibliographic record
Abstract
Using educational technology does not necessarily make medical education more effective. There are many different kinds of technology available to the contemporary medical teacher and what constitutes effective use may depend on the technology, the learning situation and many other factors. Web-based multimedia instruction (WBMI) provides learners with self-directed independent learning opportunities based on didactic material enhanced with multimedia features such as video and animations. WBMI may be used to replace other didactic events (e.g. lectures) or it may be provided in addition to other learning opportunities. Clinical educators looking to use WBMI need to make sure that it will meet both their learners' needs and the program's needs, and it has to align to the contexts in which it is used. The following 12 tips have been developed to help guide faculty through some of the key features of the effective use of WBMI in clinical teaching programs. These tips are based on more than a decade developing, using and appraising WBMI in support of surgical clerkship education across the USA and beyond and they are intended both to inform individual uses of WBMI in clinical training and to guide the strategic use of WBMI in clinical clerkship curricula.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".