Assessing young children’s self-regulation in school contexts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it