Applying principles from the cognitive theory of multimedia learning to an existing online instructional tool of the cranial nerves.
Bibliographic record
Abstract
Despite a rich literature in the field of multimedia instruction (MMI), there is a paucity of evidence-based research investigating the incorporation of multimedia design principles and efficacy evaluation in the field of medical education. The purpose of the current study was to evaluate learning outcomes when principles from cognitive theory were applied to the design of an online MMI tool. A pre-existing MMI tool of the cranial nerves was modified using principles derived from cognitive theories of multimedia learning to develop a sister tool. The principles used were segmentation, pre-training, off-loading from visual to auditory channel, and synchronizing narration with animation. The pre-existing tool served as a control and the modified sister tool, the treatment. A pilot, randomized control study was conducted with graduate and medical students followed by a post-test knowledge and preference questionnaire. We hypothesize that students using the modified sister tool will benefit from cognitive load reduction and will score higher on the post-test knowledge questionnaires as well as indicating higher preference for their MMI tool compared to the control. Grant Funding Source: Departmental
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".