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Record W2210581929 · doi:10.5539/ies.v9n1p1

A Study of the Use of Picture Books by Preschool Educators in Outlying Islands of Taiwan

2015· article· en· W2210581929 on OpenAlexvenueno aff
Ching‐Yuan Hsiao, Yang-Mei Chang

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Promotion (chess)Predictive powerMathematics educationEarly childhood educationTheme (computing)Selection (genetic algorithm)Teaching methodEarly childhoodDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

<p class="apa">The objectives of the study were to investigate the current status of applying picture books when teaching children and to also compare the differences in picture book teaching between teachers with different background variables and who are from Taiwan’s outlying islands. The researcher distributed 179 questionnaires, and after eliminating invalid questionnaires, 177 valid questionnaires were obtained, achieving a questionnaire efficiency of 98.88%. The quantitative data were analyzed by a t-test, ANOVA, Pearson Product Moment Correlation and Multiple Regression. The results were: 1. The overall status for picture book selection by early childhood educators was considered moderate to high. 2. The primary considerations for picture book selection were ‘theme’ and ‘adopting a child’s viewpoint’. 3. The overall status for the application of picture book teaching strategies among early childhood educators was considered moderate to high. 4. The application of picture book teaching strategies was centered on ‘strategies for the promotion of thinking and activities’ and ‘developing diverse abilities in children’. 5. There is a positive correlation and predictive power between selecting and using picture books by preschool educators.</p>

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.683
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.122
GPT teacher head0.400
Teacher spread0.278 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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