Playing with a goal in mind: exploring the enactment of guided play in Canadian and South African early years classrooms
Bibliographic record
Abstract
Guided play, a balanced approach to involvement in play that includes child- and adult-direction in learning activities, holds great promise for children’s effective and engaged learning in education. Recent studies in laboratory settings show benefits for academic and socio-emotional outcomes, while retaining a focus on child-centred exploration. Often, these studies feature an experimenter with one child or a small group. In contrast, educators in early years classrooms often need to support 20 children or more. To realise guided play’s promise, we need to explore the enactment of guided play in classroom settings, and how educators can engage young children in responsive ways to promote opportunities to learn in play contexts. We offer a cross-cultural comparison of guided play that occurred in 12 Canadian and 8 South African early years classrooms. Using a qualitative, thematic approach, we analysed video-recorded observations for: 1) the frequency of educator involvement in play contexts, 2) the role of the educator in those contexts, and 3) learning opportunities that emerged due to this involvement. Based on our analysis, we consider how educators can achieve guided play in classroom settings. Implications are discussed for practice, including barriers and enablers of guided play in culturally diverse settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".