From teacher-regulation to self-regulation in early childhood: an analysis of Tools of the Mind’s curricular effects
Bibliographic record
Abstract
The aim of my DPhil is to identify educational practices predictive of students' self-regulation development during early childhood. Specifically, I will analyze the Tools of the Mind preschool curriculum (Tools), which emphasizes students’ self-regulation cultivation as its paramount aim. Since its development in 1993, Tools has spread to schools in the United States, Canada, and South America. In the face of Tools' proliferation, two questions emerge: does Tools significantly improve children's self-regulation skills? And, if so, then which of its effective elements could be applied across various educational contexts? This dissertation contains two studies. In the first, I will systematically review extant Tools research and then execute a multilevel meta-analysis of the quantitative results. Study one serves three purposes: 1) to identify all studies in the existing Tools evidence base, 2) to estimate an aggregate curricular effect, and 3) to determine how that effect varies across contexts and student characteristics. Thus, study one will assess whether Tools, at the curricular level, improves students’ self-regulation. By contrast, study two will involve more granular analyses of the discrete learning activities that collectively comprise Tools. Specifically, study two will analyze child-level self-regulation and teacher-level Tools implementation data for 1145 preschool children in 80 classrooms across six American school districts. I will employ multilevel structural equation models to assess which Tools activities are associated with students’ self-regulation growth, which are associated with decline, and which exhibit no association at all. Ultimately, this dissertation features the first Tools meta-analysis as well as the first analysis of specific Tools instructional activities. It is hoped that these analyses will identify educational practices predictive of self-regulation development both within and beyond the Tools curricular context.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".