Assessing young children’s self-regulation in school contexts
Bibliographic record
Abstract
Self-regulation describes how individuals assess and adapt to demands within and across environments. Research accumulated over the past quarter century identifies self-regulation as a powerful predictor of children’s school success. However, studying young children’s self-regulation in school is challenging. Tools that are easy and efficient to administer, closely linked to curriculum and learning in classrooms, and that do not require self-reports from children are needed. Here we report on the development and validation of the Self-Regulation In School Inventory (SRISI), a teacher-report tool designed to assess typically developing young children’s self-regulation in school. Then, we present data from the SRISI that shows how different targets of self-regulation in school were related to one another, school adjustment, child gender, and achievement. Data were gathered from 28 teachers who provided ratings of 307 kindergarten children’s (age range = 4.96–6.61 years old) self-regulation using the SRISI. An exploratory factor analysis on the SRISI items distinguished three targets of self-regulation in school: ‘Emotion Regulation’, ‘Self-Regulation of/for Learning’ and ‘Socially Responsible Self-Regulation’. Path analysis confirmed the relationship between child gender and ER and SRSR, and between SRL and achievement. Findings are situated within a larger discussion concerning the assessment of young children’s self-regulation in school.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".