An Experiential Learning Management Model (ELMM) for Early Childhood Based on the Reggio Emilia Approach and Constructivist Theory
Bibliographic record
Abstract
The study creates the development of the Experiential Learning Management Model (ELMM) for early childhood education, rooted in the Reggio Emilia approach. The Reggio Emilia approach, established in the 1960s in northern Italy, emphasizes collaborative learning among children, educators, and parents. This educational philosophy cultivates an environment where children’s innate curiosity, critical thinking, and creativity are fostered. This approach challenges traditional teacher-centered models and promotes experiential learning, where children actively interact with their environment through exploration, play, and group activities. This study presents the ELMM as a practice-based teaching model grounded in Kolb’s experiential learning theory, highlighting the significance of real-world engagement, reflection, and autonomy in the learning process. It integrates emotional involvement and hands-on practice, promoting critical thinking, creativity, and problem-solving skills among young learners. The model is designed to bridge the gap between conventional teaching methods and innovative, student-centered learning, providing a comprehensive framework to enhance the quality of early childhood education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".