Is a Picture Worth a Thousand Words? Evaluating the Design of Instructional Animations in Veterinary Education
Bibliographic record
Abstract
Empirical evidence demonstrates that student learning outcomes improve when animations are developed in alignment with the design principles of the cognitive theory of multimedia learning (CTML). The extent to which these principles are used in the design of veterinary instructional animations is unknown. In this study, we reviewed the veterinary education literature for articles that discussed specific veterinary medical animations as learning resources. The 30 referenced animations accessed through this search were analyzed to determine whether they used the CTML's 11 major design principles. Analysis revealed that the animations most commonly adhered to only 4 principles: coherence, redundancy, modality, and spatial contiguity. The majority of the 11 CTML principles were used in fewer than 40% of the animations. We also examined the alignment between raters' perceptions of the effectiveness and enjoyment of the animations and adherence to the design principles. Analyses revealed that the animations deemed by raters as most enjoyable and effective did not utilize more design principles than animations they viewed as least enjoyable and effective. The results of this study indicate many missed opportunities to increase learning by developing animated learning resources according to empirically based design principles. Decisions to include specific animations in instruction should be based on whether the resources include elements that have been shown to increase learning rather than subjective perceptions of effectiveness and enjoyment.
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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 | 0.018 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".