Exploring Early Childhood Educators and Teachers’ Beliefs and Operationalization of Self-Regulation in Ontario Kindergarten Classrooms
Bibliographic record
Abstract
Self-regulation skills lay the foundation for long-term social, emotional, behavioural, and educational well-being (Zelazo et al., 2016) and develop rapidly between the ages of 3-7 years through direct teaching practices (Dignath et al., 2016). Despite the critical importance of supporting self-regulation capabilities, research shows that few teachers explicitly teach self-regulation skills (Spruce & Bol., 2015) due to a lack of conceptual clarity in empirical and practical literature (Jones et al., 2016). The Kindergarten Program (2016) in Ontario provides an opportunity to capture the voices of early years educators on this topic because of its unique teaching team. Therefore, the purpose of this exploratory study is threefold: 1) to examine the ways early years educators define self-regulation, 2) to understand the strategies and practices that early years educators use to foster self-regulation development, and 3) to articulate the ways educator training influences teacher beliefs and self-regulation practices in kindergarten. \nThis study uses a mixed-methods approach to capture the voices from Ontario’s Full Day Kindergarten teaching team. Using an adapted version of the Self-Regulated Learning Teacher Belief Scale (Lombart et al., 2009), early childhood educators (ECEs) and OCT certified teachers provided insight into early years educators beliefs, knowledge, and operationalization of self-regulation. Results from this study demonstrate that both professions hold high beliefs towards the promotion of self-regulation and children’s capabilities to self-regulate. However, ECEs and teachers have different understandings of self-regulation as well as use different strategies and practices to foster self-regulation. For example, this study finds ECEs are more prepared for teaching self-regulation because their training has specifically focused on and highlighted this area of child development, while the majority of teachers did not learn about self-regulation in their professional training. With this finding, we can now look at ways to integrate this information into the training of teachers and potentially reform parts of the teacher training program. Overall, the findings build on existing self-regulation literature and provide a new contribution by including the perspectives of early years educators.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".