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Record W4407608597 · doi:10.22452/mojem.vol12no4.1

A BIBLIOMETRIC ANALYSIS OF TECHNOLOGICAL MANAGEMENT TRENDS IN PRESCHOOL EDUCATION AND META-ANALYSIS OF THE UTILIZATION OF TECHNOLOGICAL TOOLS IN CLASSROOM

2024· article· en· W4407608597 on OpenAlexaff
A. Syukur Ghazali, Zakiah Mohamad Ashari, Joanne Hardman, Mohd Noor Idris, Wanbayuree Kaweng

Bibliographic record

VenueMalaysian Online Journal of Educational Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMeta-analysisKnowledge managementComputer scienceMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Although the usage of technology tools in education has been a much-debated topic in recent years, there has been less research on the factors that may affect their effectiveness in preschool settings. The current study methodically collected 264 empirical publications that examine the correlation between technology tools and children's development, specifically focusing on learning performance. This was achieved using a bibliographic analysis that utilises the VOSviewer software. After conducting a systematic review, a total of 6 empirical research studies were evaluated using the OpenMEE programme and ATLAS.ti 9. Researchers are particularly interested in studying the bibliographic networks related to children's development, preschool environment, and technological utilisation. The meta-analysis results indicate that establishing and maintaining engaging and influential technological activities in the classroom can lead to improved individual development. Future research is recommended to investigate the role of various parties, such as families, as moderators in influencing children's development on integrating ICT into education. Encouraging the early use of technology in the classroom can support national policies focused on fostering communities with advanced digital skills and cultivating competent future leaders.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0930.162
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.400
Teacher spread0.311 · 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

Labeled directly by 2 models reading the full record.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venueMalaysian Online Journal of Educational ManagementSame topicGender and Technology in EducationCategoryBibliometricsFrench-language works237,207