Animated Cell Biology: A Quick and Easy Method for Making Effective, High-Quality Teaching Animations
Bibliographic record
Abstract
There is accumulating evidence that animations aid learning of dynamic concepts in cell biology. However, existing animation packages are expensive and difficult to learn, and the subsequent production of even short animations can take weeks to months. Here I outline the principles and sequence of steps for producing high-quality PowerPoint animations in less than a day that are suitable for teaching in high school through college/university. After developing the animation it can be easily converted to any appropriate movie file format using Camtasia Studio for Internet or classroom presentations. Thus anyone who can use PowerPoint has the potential to make animations. Students who viewed the approximately 3-min PowerPoint/Camtasia Studio animation "Calcium and the Dual Signalling Pathway" over 15 min scored significantly higher marks on a subsequent quiz than those who had viewed still graphics with text for an equivalent time. In addition, results from student evaluations provided some data validating the use of such animations in cell biology teaching with some interesting caveats. Information is also provided on how such animations can be modified or updated easily or shared with others who can modify them to fit their own needs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".