Play in the Grade One Classroom: An Exploration of Teacher Beliefs, Classroom Organization, and Obstacles to Implementation in Quebec
Bibliographic record
Abstract
A mixed methods investigation into Grade One teachers’ beliefs and practices about play in the classroom was conducted in a French public school board in Quebec. Forty-three teachers completed questionnaires, while a subset of 10 classrooms were photographed, and open-ended interviews were conducted with 7 teachers. Correlation between beliefs, practices, and experience teaching showed that ‘learning through play’ was positively associated with belief in ‘educational toys and manipulation’ (rs = 0.313, p < 0.05), and greater frequency of teacher self reported play activities (r = 0.524, p < 0.01). Teachers who had more experience teaching Grade One, believed less in ‘learning through play’ (r = -0.341, p < 0.05) and reported using fewer play practices in their classes (r = 0.365, p < 0.05). Total years teaching was also negatively correlated with belief in ‘learning through play’ (r = -0.410, p < 0.01). Photographs revealed minimal variation between classrooms, with some elements of play in half of the classrooms. Interviews revealed that teachers found play to be an effective learning strategy, that they primarily used games as play activities in their classrooms, and that school personnel and personal experience supported their use of play, while lack of time, budget and materials limited the amount of play activities implemented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".