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Record W2809441674 · doi:10.5539/mas.v12n7p27

The Impact of Providing Computer Language Experience on the Reading Readiness among Preschool Children

2018· article· en· W2809441674 on OpenAlexvenueno aff
Ali Ahmad Bani Hamad

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsRecallReading (process)Computer scienceMathematics educationScale (ratio)PsychologyDevelopmental psychologyCognitive psychologyLinguistics

Abstract

fetched live from OpenAlex

This study aim to identify the effect of computer literacy on reading readiness among preschool children. The study sample consisted of (49) preschool children studying at the First University Kindergarten distributed into to two groups: experimental and control. The language experience of the experimental group was presented to students through using the computer for two years. A program has been designed for this purpose using the Multimedia Builder software. In order to answer the study questions, the mathematical averages, standard deviations, ANCOVA and MANCOVA analysis were used. The results of the study showed that there was an effect of computer experience in the six dimensions of reading readiness (visual recognition, auditory distinction, understanding, information, auditory recall, and visual recall) and this effect is related to the group variable. However, there were no statistically significant effect of sex variable over all the dimensions of the Reading readiness scale.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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