The Analysis of Teachers’ Competence in Participating the In-Service Training Program of Inclusive Education in Indonesia
Bibliographic record
Abstract
Theory, strategy, learning method, technology, and curriculum of inclusive education for both regular children and children with special needs (CWSN) are changing from time to time. Teachers require In-Service Training (IST) which enables them to adapt to these changes. One of the alternative ways for teachers who were already employed to obtain a new development access in education and educational technologies is to get IST. This research aimed to classify the teachers’ competence in inclusive schools based on their participation in the In-Service Training program of inclusive education. The research subjects were the 38 inclusive school teachers, taken by purposive random sampling. The data was collected by using questionnaire and analyzed by using descriptive and parametric statistic. The results reveal that there was a significant difference in pedagogic competence of teachers based on their participation in the In-Service Training (IST) program of inclusive education. The more often the teachers participate in the In-Service Training program, the better their pedagogic competence can be.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".