MétaCan
Menu
Back to cohort
Record W2090502480 · doi:10.1080/03004279.2013.848916

Playing with maths: implications for early childhood mathematics teaching from an implementation study in Melbourne, Australia

2014· article· en· W2090502480 on OpenAlexfundno aff
Caroline Cohrssen, Collette Tayler, Dan Cloney

Bibliographic record

VenueEducation 3-13 · 2014
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersAustralian Research CouncilState Government of VictoriaDepartment of Education and Training, Queensland GovernmentUniversity of Toronto ScarboroughQueensland Government
KeywordsEarly childhoodMathematics educationEarly childhood educationCurriculumSuitePeriod (music)Australian CurriculumPedagogyPsychologyDevelopmental psychologyProject commissioningPolitical science

Abstract

fetched live from OpenAlex

The Early Years Learning Framework for Australia governs early childhood education in the years before school in Australia. Since this framework is not a curriculum, early childhood educators report uncertainty regarding what mathematical concepts to teach and how to teach them. This implementation study, positioned within the broader E4Kids study, explored the enactment of a suite of play-based mathematics activities by five early childhood educators in different settings over a seven-month period. The educators' approaches to incorporating the activities are discussed in light of the reported implementation frequency and the duration of activities. A regression analysis predicted significant changes in children's Fluid Intelligence/Reasoning associated with attending high-implementation programmes. Recommendations are made for further investigation of the enactment of mathematics activities in early childhood settings and for the provision of professional learning opportunities that focus on supporting children's concept development as well as their mathematical skills.

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)0.000
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 score0.218
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.050
GPT teacher head0.398
Teacher spread0.348 · 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

Citations21
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEducation 3-13Same topicCognitive and developmental aspects of mathematical skillsFrench-language works237,207