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Record W2904383589 · doi:10.5430/wje.v8n6p32

Using Learning Centers to Improve the Language and Academic Skills of Preschool Children

2018· article· en· W2904383589 on OpenAlexvenueno aff
Özgün Uyanık Aktulun, Gözde İnal Kızıltepe

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationLiteracyMultimethodologyAcademic skillsAcademic yearResearch designTest (biology)Sample (material)Medical educationDevelopmental psychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

A mixed-methods research design was employed to investigate the impact of use of learning centers to supportlanguage and academic skills of children aged 61–72 months. The sample of quantitative data of the study consistedof 70 children (35 in the experimental group and 35 in the control group). In the quantitative dimension of the study,data were collected using the “Kaufman Survey of Early Academic and Language Skills,” “Progress in Maths 6 Test,”and the “Control List for the Evaluation of the Print Awareness of Pre-School Children” scales. In the qualitativedimension of the study, semi-structured interviews were conducted with the teachers of the experimental groupthrough the “Teacher Interview Form” developed by the researchers. During the implementation period, learningcenters were established and organized in such a way that the 35 children in the experimental group could use themfor about 75–90 minutes every day for eight weeks. The results obtained from the study reveal that arrangementsmade in the learning centers provide important contributions to the development of children’s language, literacy andmathematics 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 score0.417
Threshold uncertainty score0.163

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 head0.013
GPT teacher head0.344
Teacher spread0.332 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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