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Record W4384930756 · doi:10.1177/1476718x231186613

Educator understanding of self-regulation and implications for classroom facilitation: A mixed methods study

2023· article· en· W4384930756 on OpenAlexaffabout
C. Burgess

Bibliographic record

VenueJournal of Early Childhood Research · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsAlgoma University
Fundersnot available
KeywordsPsychologyMental healthSelf-controlAggressionCognitionVariety (cybernetics)AnxietyDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

There is growing concern about the mental health and resilience of today’s children and difficulties with self-regulation are implicated in educational outcomes, cognitive problems, internalizing problems such as depression and anxiety, externalizing problems such as aggression, and physical health problems. Self-regulation is a growing topic of interest in a variety of disciplines and there are 447 different interpretations of what self-regulation means in the literature, which makes it difficult for educators to interpret and apply it in their classrooms. Due to advances in neuroscience, the Ontario Ministry of Education shifted toward a neurophysiological framework for the Self-Regulation and Well-Being Frame of the Kindergarten Program. The current study examined which frameworks Ontario kindergarten educators were using by analyzing the ways they described and facilitated self-regulation in the classroom through surveys, interviews, report cards, and classroom observations. Findings revealed that educators: have little experience and training with resources aligned with the Kindergarten Program’s approach to self-regulation, describe self-regulation as self-control, and facilitate self-regulation using a learning strategies approach. Educators were observed using fewer than a third of ministry self-regulation recommendations in the classroom. Implications and recommendations for aligning educator practices with the Kindergarten Program’s framework are discussed.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.216
GPT teacher head0.468
Teacher spread0.252 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

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