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Record W4413233315 · doi:10.23887/ijerr.v8i1.90137

A Systematic Literature Review of Internal Quality Assurance in Early Childhood Education in Developed and Developing Countries

2025· article· en· W4413233315 on OpenAlexaff
Yuli Pujianti, Siti Aminah, Entin Nuryati, Agus Mulyanto, Aimi Farina Sariff

Bibliographic record

VenueIndonesian Journal Of Educational Research and Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsQuality assuranceDeveloping countryQuality (philosophy)BusinessPsychologyPolitical scienceEconomic growthEngineeringOperations managementEconomicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Internal Quality Assurance (IQA) is a critical component in early childhood education (ECE) to ensure that learning services meet established standards and deliver high-quality educational experiences for young children. This study aims to analyze the approaches, implementation practices, and challenges of IQA in ECE institutions through a Systematic Literature Review (SLR). The data were obtained from peer-reviewed articles published between 2014 and 2024 indexed in the Scopus database. Thematic analysis was employed to identify recurring patterns and key themes related to three dimensions of quality: structural, process, and outcome. The findings reveal that countries with more established education systems tend to integrate continuous evaluation and professional development systematically, whereas nations with limited capacity face significant challenges in terms of funding, training, and infrastructure. The study concludes that effective IQA requires a contextualized and collaborative approach, supported by consistent policy frameworks to enhance the global quality of ECE. These insights contribute conceptually to the development of adaptive and responsive quality assurance systems across diverse educational settings.

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)0.009
metaresearch head score (Gemma)0.008
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: Review · Consensus signal: Review
Teacher disagreement score0.124
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.037
GPT teacher head0.431
Teacher spread0.395 · 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
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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