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Record W2955633954 · doi:10.1080/02568543.2019.1609144

Kindergarten Expectations and Outcomes: Understanding the Influence of Educator and Child Expectations on Children’s Self-Regulation, Early Reading, and Vocabulary Outcomes

2019· article· en· W2955633954 on OpenAlexafffund
Kristy Timmons

Bibliographic record

VenueJournal of Research in Childhood Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyVocabularyReading (process)Developmental psychologyVocabulary developmentPath analysis (statistics)Early childhoodTest (biology)Early childhood educationTeaching methodMathematics education

Abstract

fetched live from OpenAlex

This research examines the influence of educator and child expectations on children’s self-regulation, early reading, and vocabulary outcomes over two time points. Thirty educators (15 early childhood educators [ECEs] and 15 teachers) and 149 kindergarten children participated in the research. The educators participated in an expectation ranking questionnaire. Data collection with the children included an interview task and standardized and nonstandardized assessments. Results indicate that ECE and teacher expectations were congruent at Time 1 but dissonant at Time 2, with ECEs having significantly higher expectations for children’s self-regulation, early reading, and vocabulary outcomes at Time 2. Path analyses were run to simultaneously test for direct and indirect (mediating) effects. Findings revealed that ECE and child expectations had significant positive direct effects on outcomes. The only significant direct effect from teacher expectations to child outcomes was for vocabulary, and this was a negative direct effect. This research has important and direct application to practice through professional development with pre- and in-service educators, specifically in considering the practices of high expectation educators.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.113
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.362
Teacher spread0.335 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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