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Record W1868826336 · doi:10.1177/1053815115598842

Effects of an Animated Book Reading Intervention on Emergent Literacy Skill Development

2015· article· en· W1868826336 on OpenAlexaff
Erin Schryer, Elizabeth Sloat, Nicole Letourneau

Bibliographic record

VenueJournal of Early Intervention · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of CalgaryUniversity of New Brunswick
Fundersnot available
KeywordsReading (process)Shared readingLiteracyPsychologyRhymeVocabularyIntervention (counseling)Phonological awarenessVocabulary developmentEmergent literacyMathematics educationPhonemic awarenessDevelopmental psychologyPedagogyTeaching methodLinguistics

Abstract

fetched live from OpenAlex

Early language and reading experiences are known to predict later reading success. Interactive shared reading activities particularly benefit children’s emergent literacy development. Converging research has begun to show that certain educational television programs can significantly influence early literacy skill acquisition. There is a need, however, to combine interactive shared reading with the educational television production techniques known to purposefully facilitate emergent literacy. This research begins to address that need by pilot testing a research-based animated book reading intervention developed specifically to promote the vocabulary, alphabet knowledge, print concept, and rhyme knowledge of preschoolers in child care between the ages of 3 and 5 years. Employing a quasi-experimental pre- and posttest research design, results indicate that children in the experimental group made greater gains on standardized emergent literacy measures compared with children in a control group. Early findings suggest that the animated book reading intervention may be a feasible and effective way for child care educators to teach early reading 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 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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.340
Teacher spread0.318 · 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
Published2015
Admission routes1
Has abstractyes

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