Computer animations in medical education: a critical literature review
Bibliographic record
Abstract
CONTEXT: Animations can depict dynamic changes over time and location, and illustrate phenomena and concepts that might otherwise be difficult to visualise. However, animations may not always be effective and educators who use animations must understand the principles that govern their use. OBJECTIVES: This review aims to illustrate potential applications of animations in medical education, to identify evidence-based principles for their design and use, and to propose an agenda for future research. METHODS: We searched MEDLINE, PsychINFO and EMBASE for articles describing the use of computer animations in medical education. We reviewed and summarised all identified original research studies comparing animations with an alternative computer-based or non-computer-based format. We also selectively reviewed non-medical education research on the use of computer animations. RESULTS: Medical educators have used animations in a variety of computer-assisted learning applications, but few comparative studies have been published and the evidence is inconclusive. Research outside medical education shows conflicting results for studies comparing animations with static images. This may reflect differences in cognitive load induced by animation, or differences in the type of motion being illustrated. The benefits of animations may also vary according to learner characteristics such as prior knowledge and spatial ability. Features of animation that appear to facilitate learning include permitting learner control over the animation's pace, allowing learners to interact with animations and splitting the animation activity into small chunks (segmenting). CONCLUSIONS: Existing medical education research does little to inform the use of animations. Research is needed to confirm and extend non-medicine research to ascertain when to use animations and how to use them effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".