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Record W2772505812 · doi:10.5287/ora-kzjaoxm8j

From teacher-regulation to self-regulation in early childhood: an analysis of Tools of the Mind’s curricular effects

2017· dissertation· en· W2772505812 on OpenAlexaboutno aff
Alexander Macomber Baron

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2017
Typedissertation
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumExtant taxonMultilevel modelPsychologyAssociation (psychology)Mathematics educationDevelopmental psychologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.335
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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