Leveraging assessment to promote kindergarten learners’ independence and self-regulation within play-based classrooms
Bibliographic record
Abstract
Currently, kindergarten education is shaped by two priorities: (1) the recognition that early learning must maintain a developmental orientation and support socio-personal growth; and (2) a growing emphasis on standards-based curriculum and the use of assessment to support children’s learning. While some researchers have argued these two priorities are counter-related, research demonstrates the potential to embed these goals through play-based pedagogies. The purpose of this paper is to explore how kindergarten teachers leverage assessment practices, particularly Assessment as Learning (AaL), to support children learning within play-based classrooms. Centrally, we argue that a focus on AaL and self-regulation might be the fulcrum that hinges play-pedagogies with standards-based education and assessment mandates, helping to diminish the divide between these two priorities. Data are drawn from 20 kindergarten classrooms via initial interviews, observations and video-elicitation teacher interviews. Findings identify how kindergarten teachers are productively using assessment to promote learner independence within play-based classrooms.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".