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Record W4407972532 · doi:10.1080/02568543.2025.2455634

The Impact of Touchscreens on Early Learning: A Meta-Analysis

2025· article· en· W4407972532 on OpenAlexafffund
atefeh taheryan

Bibliographic record

VenueJournal of Research in Childhood Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMeta-analysisDevelopmental psychology

Abstract

fetched live from OpenAlex

Due to their interactive features, touchscreen technologies are used prevalently among young children. Many early childhood education (ECE) studies have been conducted on the impact of touchscreens on early learning, but these studies have reported divergent findings. Further, there is a dearth of research investigating the factors contributing to the improvement of early learning with touchscreens. To fill these gaps, our meta-analysis examined 57 empirical articles to investigate the pooled effect of touchscreen technology on early learning and potential moderators on early learning with touchscreens. The findings showed a significant learning effect linked to touchscreens (d = 0.48). Additionally, the analysis of moderators indicated that learning domains, feedback from adults, and the level of touchscreen integration played a moderating role in the impact of touchscreens on early learning. A discussion with implications and future research is provided.

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)0.019
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.047
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.116
GPT teacher head0.490
Teacher spread0.374 · 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 designMeta-analysis
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

Citations2
Published2025
Admission routes2
Has abstractyes

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