Using web‐based animations to teach histology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We have been experimenting with the use of animations to teach histology as part of an interactive multimedia program we are developing to replace the traditional lecture/laboratory-based histology course in our medical and dental curricula. This program, called HistoQuest, uses animations to illustrate basic histologic principles, explain dynamic processes, integrate histologic structure with physiological function, and assist students in forming mental models with which to organize and integrate new information into their learning. With this article, we first briefly discuss the theory of mental modeling, principles of visual presentation, and how mental modeling and visual presentation can be integrated to create effective animations. We then discuss the major Web-based animation technologies that are currently available and their suitability for different visual styles and navigational structures. Finally, we describe the process we use to produce animations for our program. The approach described in this study can be used by other developers to create animations for delivery over the Internet for the teaching of histology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it