Understanding and Supporting Student Thriving in the Ontario Kindergarten Classroom
Bibliographic record
Abstract
Human thriving is often counterposed to survival and perceived as the ultimate achievable goal on a spectrum of human development and positive functioning. However, few researchers have attempted to directly tackle thriving within the educational context. Furthermore, student thriving has been largely overlooked within the literature on Kindergarten and early childhood education. Given that children’s early experiences lay the foundation for lifelong learning, development, health, and well-being, the importance of supporting children to thrive within the early years of life and school should not be overlooked. The purpose of this qualitative study was to understand how student thriving is conceptualized and can be supported within the context of the Ontario Kindergarten classroom from the perspective of three key stakeholders (students, educators, and parents). Through this understanding, a theory grounded in the data was developed. Six Kindergarten students, three educators (early childhood educator, Kindergarten teacher, and head administrator), and seven parents provided a rich understanding of Kindergarten student thriving and its supports. Although the language used to convey these ideas differed among the stakeholder groups, numerous commonalities and consistencies were observed across the three perspectives. These consistent themes included: positive relationships; in[ter]dependence; value of play; holistic development; and individualized experiences and diverse trajectories of thriving. Findings from this study illustrate that thriving is a complex and multi-dimensional construct, and that supporting student thriving in the Kindergarten classroom requires a multi-faceted, integrated approach. Because no prior research has endeavoured to explore thriving within this context, and early childhood more generally, this research provides an essential starting point from which to better understand human thriving at this early stage of life and across the lifespan. Furthermore, the findings from this study offer insight into the indicators and supports of student thriving within the Kindergarten classroom, which have implications for both research and practice.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".