Teacher RePlay and Children ReAct: pilot testing a formative toolkit to support playful learning in the classroom
Bibliographic record
Abstract
Playful learning has seen a resurgence of interest in the past decade, particularly in contexts where play is not traditionally part of a teacher’s repertoire. Teachers interested in exploring the integration of play in their classrooms need formative tools and resources that help them to reflect and assess their own practice and their ability to create a playful learning experience for their students. This study presents the results of two rounds of pilot testing in three countries for Teacher RePlay, a new open-source toolkit designed to support teachers interested in reflecting on and deepening their learning through play practice. The toolkit includes the main Teacher RePlay observation protocol for teachers, as well as Children ReAct, a complementary protocol for a photo-elicited focus group discussion with children, intended to directly assess children’s experiences and reflections on learning through play. Upon observation, teachers receive customized coaching suggestions and tips designed to strengthen their learning through play practice. Initial results from the piloting indicate that the toolkit holds strong potential for teachers interested in better understanding and deepening their playful learning practice. This paper discusses the development, validation, successes, and challenges of the Teacher RePlay toolkit, and identifies future directions for its use.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| 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".