Learning Stories ako metóda formatívneho hodnotenia v českom predprimárnom vzdelávaní
Bibliographic record
Abstract
In the last decade, formative assessment methods have gained international prominence in pre-primary education. The Learning Stories method is a formative assessment method which was designed specifically for pre-primary education. It was developed in the late 1990s in New Zealand and has been used in Canada, Australia, in the United States and some Western European countries. The aim of the article is to introduce the results of a two-year action research (2019–2021) that focused on the implementation of the Learning Stories method in six Czech pre-schools and the documentation of its impact on teacher's assessment processes and children's learning. The research data indicate that this method is an effective tool that reinforces child-centred approach to assessing children’s learning. The method enhanced the teachers´ understanding of the importance of formative assessment, guided them to re-evaluate their concepts of assessing children's development and learning. The method enabled the teachers to have a deeper understanding of children's learning processes and increased participation of children in assessing their own learning.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".