Teaching Emotion Regulation to Kindergarten Students: Teachers' Perspectives and Pracctices
Bibliographic record
Abstract
The purpose of this qualitative research project was to learn what perspectives a small sample of kindergarten teachers in Ontario hold in regards to teaching emotion regulation, a skill that is proven to be vital for students’ social-emotional development, and how these perspectives inform their practice. Data was collected through semi-structured interviews with two kindergarten teachers who work in publicly funded schools in Ontario. Interview transcripts were analysed and the emerged themes were organized into four main themes: the strategies used by the participants to teach their students to regulate their emotions; the outcomes they observed; the resources that helped to inform their practice; and the barriers and challenges they were facing in the area of teaching emotion regulation. Findings suggest that although teachers’ training programs were not identified as a main resource in this area, participants sought support from their colleagues to improve their practices and learn effective strategies to teach emotion regulation. Implications for the education community and personal practice are discussed, and recommendations are made for the Ontario College of Teachers and the faculties of education across Ontario to increase the emphasis being put on the social-emotional development of children in teachers’ preparation programs.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".