The Learning Management Models in the 21st Century for Early Childhood Teachers
Bibliographic record
Abstract
The goal of this research is to develop a 21st-century learning management model tailored for early childhood teachers in Roi Et Province. The sample group includes teachers of early childhood education. A simple random sample from the sample is chosen to achieve this. 400 persons. The research employed questionnaires, an assessment form, and learning tests for early childhood teachers as tools. The data analysis utilized statistical measures like averages, standard deviations, and other factors. SEM, on the other hand, analyzes linear structural relationships. The results showed that: The progression of learning management among early childhood educators includes: Teacher Professional Competency (CoPT) Noble Truth 4 (NoTF) Teacher Spirit (SopT) Inspiration to have a public mind (IPS) Learning Management Behavior (BML) and the behavior of using learning innovations (UILM) The driving force of civic-mindedness. (IPS) It has a direct impact on the use of learning innovations. (UILM) For early childhood educators, 87.50% In addition, it can offer an additional elucidation of the variability of the internal latent variables. 2 The body exemplifies the principle of Inspiration with a public spirit (IPS) at 82.70% and demonstrates learning management behavior (BML) Percentage at 98.60%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".