Applying multimedia design principles enhances learning in medical education
Bibliographic record
Abstract
CONTEXT: The Association of American Medical Colleges' Institute for Improving Medical Education's report entitled 'Effective Use of Educational Technology' called on researchers to study the effectiveness of multimedia design principles. These principles were empirically shown to result in superior learning when used with college students in laboratory studies, but have not been studied with undergraduate medical students as participants. METHODS: A pre-test/post-test control group design was used, in which the traditional-learning group received a lecture on shock using traditionally designed slides and the modified-design group received the same lecture using slides modified in accord with Mayer's principles of multimedia design. Participants included Year 3 medical students at a private, midwestern medical school progressing through their surgery clerkship during the academic year 2009-2010. The medical school divides students into four groups; each group attends the surgery clerkship during one of the four quarters of the academic year. Students in the second and third quarters served as the modified-design group (n=91) and students in the fourth-quarter clerkship served as the traditional-design group (n=39). RESULTS: Both student cohorts had similar levels of pre-lecture knowledge. Both groups showed significant improvements in retention (p<0.0001), transfer (p<0.05) and total scores (p<0.0001) between the pre- and post-tests. Repeated-measures anova analysis showed statistically significant greater improvements in retention (F=10.2, p=0.0016) and total scores (F=7.13, p=0.0081) for those students instructed using principles of multimedia design compared with those instructed using the traditional design. CONCLUSIONS: Multimedia design principles are easy to implement and result in improved short-term retention among medical students, but empirical research is still needed to determine how these principles affect transfer of learning. Further research on applying the principles of multimedia design to medical education is needed to verify the impact it has on the long-term learning of medical students, as well as its impact on other forms of multimedia instructional programmes used in the education of medical students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".