A Systematic Literature Review of Internal Quality Assurance in Early Childhood Education in Developed and Developing Countries
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".