Experiences of Pre-School Teacher Candidates on the Development and Application of Slowmation in Science Education
Bibliographic record
Abstract
The purpose of the study is to examine the pre-school teacher candidates’ experiences in developing and using a Slowmation as an educational technique with young children. This study also aims at providing the pre-school teachers’ reflections on the whole development and application process. The study was carried out with 65 pre-school teacher candidates (46 of them are from day classes and 19 of them are from evening classes) who were studying in the pre-school education department in the spring term of the 2017–2018 academic year. The researcher used phenomenological research method which relied on these data resources: field notes, open-ended questions, videos and photos and semi-structured interviews. It was concluded that pre-school teacher candidates have developed their skills in using the Slowmation technique during the course. The researchers also concluded that the Slowmation technique, despite having some limitations, has a positive effect on pre-school students’ learning process of the science concepts and it could contribute significantly to their skill development process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".