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Record W4292772462 · doi:10.3102/1442742

A Review of Curricula: Examining Early Childhood Policies and Practices to Foster Self-Regulation

2019· review· en· W4292772462 on OpenAlexaboutno aff
Kristy Timmons

Bibliographic record

VenueProceedings of the 2019 AERA Annual Meeting · 2019
Typereview
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumComputer sciencePsychologyKnowledge managementPedagogy

Abstract

fetched live from OpenAlex

Attention ability is an area of self-regulation that is particularly prominent in the literature on children's transition to and experiences in early childhood classrooms Attention abilities play a strong role in supporting children's ability to focus on relevant learning activities, while inhibiting distracting stimuli Based on the emerging literature on the important role of self-regulation in supporting learning and healthy child development, policy makers have made efforts to include self-regulation skills in practice and policy documents worldwide Despite these efforts, there is limited understanding of what self-regulation is and how best to support it in the day-today life of a classroom. Thus, it becomes important to systematically analyze and examine these efforts in a critical way. Therefore, the objective of this research is twofold: 1) to examine how self-regulation is discussed and promoted in early childhood curriculum frameworks, and 2) to develop and disseminate recommendations based on improving clarity of the practice-orientated conceptualization of self-regulation derived from early years policy documents. In order to understand how this shift towards fostering self-regulation in early childhood is developing in Ontario, our team engaged in a scan of relevant documents generated in Ontario. After mining down into how self-regulation is represented in Ontario documents, we also examined other provinces and international documents, in order to provide a context for our findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.347
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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