An Exploration of Child-Friendly Learning Environment Characteristics in Sri Lankan Preschools
Bibliographic record
Abstract
This study explores the characteristics of child-friendly learning environments in Sri Lankan preschools by examining how the key dimensions of the Child-Friendly School (CFS) framework are reflected in preschool settings in the Batticaloa District in Sri Lanka. Employing a mixed-methods research design, the study integrates quantitative and qualitative approaches to provide a comprehensive understanding of current preschool practices. Quantitative data were collected through structured questionnaires administered to preschool teachers, while qualitative insights were obtained through in-depth interviews and systematic observations conducted at selected preschool sites. Descriptive statistical analysis was used to examine quantitative data, and narrative analysis was applied to interpret qualitative findings. From the 99 preschools in the Batticaloa zone, a 10% sample (13 preschools) was selected using stratified random sampling. Purposive sampling was applied to select 5% of teachers, 10% of principals, and 10% of preschools to ensure the inclusion of key informants relevant to the study’s objectives. The findings reveal several critical challenges in establishing child-friendly learning environments. Teachers demonstrated limited awareness of child-friendly pedagogical principles and insufficient training in addressing the needs of children with disabilities or special learning requirements. Furthermore, the characteristics of child-friendly learning environments recommended by UNICEF were not consistently incorporated into preschool practices. These gaps highlight significant areas for improvement and indicate the need for systematic capacity-building, targeted professional development, and strengthened policy implementation. Overall, the study contributes valuable insights into the current status of preschool learning environments in Sri Lanka and underscores the need for enhanced alignment between policy expectations and on-the-ground practices. Strengthening child-friendly environments in preschools will require coordinated efforts among educators, administrators, policymakers, and community stakeholders to ensure that early learning spaces fully support the holistic development and rights of young children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".