Using 3D Animations to Teach Intracellular Signal Transduction Mechanisms: Taking the Arrows out of Cells
Bibliographic record
Abstract
Traditional methods of teaching intracellular biological processes and pathways use figures or flowcharts with the names of molecules linked with arrows. Many veterinary students, presented with such material, simply memorize the names or chemical structures of the molecules and are then likely to forget the material once the examination is completed. To address this problem, the authors designed, created, and field-tested new teaching media that incorporate realistic three-dimensional (3D) animations depicting the dynamic changes that occur in intracellular molecules during cellular activation. Testing found that veterinary students taught using traditional teaching media (e.g., lectures, handouts, textbooks) are proficient in memorizing the names and order of intracellular molecules but unable to appreciate the interactions between these elements or their spatial relationships within cells. In contrast, more than 90% of veterinary students taught using 3D animations not only recall the facts about the intracellular elements but also develop accurate mental images of the interactions among these molecules and their spatial relationships. These findings strongly suggest that the comprehension of complex biological processes by veterinary students can be enhanced by the use of dynamic 3D depictions of these processes in the classroom.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".