More Play, Please: The Perspective of Kindergarten Teachers on Play in the Classroom.
Bibliographic record
Abstract
e past decade has seen an increase in research documenting the benets of children learning through play. However, the amount of play in American kin-dergarten classes remains on a steady decline. is article compares the ndings from a netnographic study of seventy-eight kindergarten teachers ’ message board discussions about play in kindergarten with those of more traditional studies and nds the teachers ’ discussions in broad agreement with past research. e results further demonstrate that kindergarten teachers feel pressures from other teachers, principals, and school policies to focus on academic goals and that these pressures lead them to limit play. e author argues for further research to develop eec-tive strategies to help teachers include play in kindergartens rather than merely increasing teacher awareness of the benets of play. She details how a netnographic approach can complement traditional methods for understanding how teachers treat play in their classrooms. Key words: kindergarten; netnography; No Child Le! Behind Act (NCLB); play-based teaching; Social Ecological eory (SET) Play in American Kindergartens In the past decade, play research has witnessed a rise in two seemingly contradictory trends. First, the research increasingly shows that play expedites a variety of social, cognitive, motor, and linguistic improvements (Eberle 2011; Fisher et al. 2011). Social play allows children to become more creative and more adept at explaining meaning verbally, more successful at manipulating dierent symbol systems, and more condent when experimenting with new activities (Bjorklund and Gardiner 2011; Eberle 2011; Pellegini 2009). In school settings, teachers gently guide play, using play-based teaching and learning activities to promote curricular goals while maintaining the critically important aspects of play—such as children’s intrinsic motivation to engage in play (Bordova,
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".