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Record W3137240938 · doi:10.1080/03004430.2021.1885391

Vocabulary enrichment using an E-book with and without kindergarten teacher’s support among LSES children

2021· article· en· W3137240938 on OpenAlexaff
Ofra Korat, Shifra Atishkin, Ora Segal-Drori

Bibliographic record

VenueEarly Child Development and Care · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsVocabularyPsychologyClass (philosophy)Vocabulary developmentMathematics educationIntervention (counseling)Preschool educationDevelopmental psychologyLinguisticsTeaching methodComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We examined an intervention in kindergarten using an e-book for vocabulary enrichment. In programme (a), the children read the e-book with a dictionary and the teacher’s support. In programme (b), the children read the e-book with the dictionary independently. In programme (c), the children read the e-book without a dictionary (control). The participants included 103 children (aged 5–6) from LSES families. They read the e-book in the kindergarten class six times. The children were tested pre, post 1 and post 2, on story focal words at the receptive, explanation and production level. Children who read the e-book with the dictionary and the teacher’s support learned more words than those, who read the e-book with the dictionary independently, and more than the control. Achievements were maintained after one month. Children with an initial low level progressed more than those with a high level. The findings and their implications 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2021
Admission routes1
Has abstractyes

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