Stop Motion Animation for Preschoolers by Master Teachers
Bibliographic record
Abstract
This research was conducted with the following aims: to enable master teachers to develop electronic tales for preschoolers using Stop Motion techniques according to the Early Childhood Imagineering Model (ECIM) process; to evaluate the quality of these electronic tales; to evaluate master teachers’ ability to develop these tales; to evaluate the effect of master teachers’ transfer of the development of electronic tales to early childhood education (ECE) student teachers; and to evaluate master teachers’ satisfaction with their development of these electronic tales. The sample comprised 24 ECE instructors in higher education, who were selected as the master teachers, and 480 ECE student teachers. The findings revealed that the master teachers were able to develop 24 electronic tales for preschoolers using the Stop Motion technique according to the Early Childhood Imagineering Model (ECIM) process. They also indicated that the quality of the electronic tales, master teachers’ ability to develop these tales and transfer their development to student teachers, and master teachers’ level of satisfaction with the development of these electronic tales, were all at a high level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".