Whiteboard animation: A potential teaching tool for health science education.
Bibliographic record
Abstract
Background: Combining visual thinking and storytelling makes whiteboard animation an effective educational tool. However, the impact of whiteboard animation is understudied in health science education. This literature review explored the use and impact of whiteboard animation on teaching in health science education. Method: A comprehensive electronic literature search was conducted in 5 databases: PubMed, Google Scholar, CINAHL, Web of Science, and Education Research Complete to identify full-text research articles published in English between 2013 and 2024. Articles were screened to match inclusion criteria, and data were extracted from the eligible studies. Results: After 2 rounds of screening, 6 articles were included in the review, all focussing on evaluating the impact of whiteboard animations in dental, medical, and other health science education. All studies reported positive impacts on student satisfaction and knowledge acquisition. A correlation between the number of video views and students' longitudinal exam performance was also reported. Discussion and Conclusion: The concise and engaging animations explaining concepts in a storytelling manner offer an alternative mode of presenting teaching material, reducing extrinsic cognitive loads on the learners. Further studies are needed to evaluate the impact of this powerful tool on health science education.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".