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Record W1483575264 · doi:10.1177/1476718x15582096

Children’s mathematical and verbal competence in different early education and care programmes in Australia

2015· article· en· W1483575264 on OpenAlexfundno aff
Claudia Hildenbrand, Frank Niklas, Caroline Cohrssen, Collette Tayler

Bibliographic record

VenueJournal of Early Childhood Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersDepartment of Education and Training, Queensland GovernmentAustralian Research CouncilDeutscher Akademischer AustauschdienstUniversity of Toronto ScarboroughQueensland GovernmentRobert Bosch Stiftung
KeywordsAttendanceEarly childhoodEarly childhood educationCompetence (human resources)Child careDevelopmental psychologyPsychologyDay careChild developmentLongitudinal studyMathematics educationMedical educationMedicineNursingSocial psychology

Abstract

fetched live from OpenAlex

This study investigated the relationship between children’s attendance at different types of early childhood education and care programmes and their mathematical and verbal skills. Analyses of data from 1314 children participating in an Australian longitudinal study, the E4Kids project, revealed no relationship between children’s verbal ability and the early childhood education and care programme attended, but mathematics results tell a different story. At the first measurement, children who consistently attended only informal care outperformed children who either consistently attended a formal early childhood education and care service type or attended a mix of formal and informal care. The development of mathematical and verbal competencies between first and second measurements, 1 year later, did not differ between children who attended different types of early childhood education and care. Early childhood educators in Australia are required to provide programmes that incorporate both mathematical concepts and language development. However, many early childhood educators describe uncertainty about how to support children’s mathematical learning. Further professional development and support in this area is necessary.

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 score0.238
Threshold uncertainty score0.416

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.406
Teacher spread0.334 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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