Improvement of Teacher Competence in Special Schools for Deaf and Intellectually Disabled Children to Enhance the Quality of Inclusive Education
Bibliographic record
Abstract
This research aims to analyze the human resource management (HRM) strategy at Fadhilah Special School for children with hearing impairment and intellectual disabilities in an effort to improve the quality of inclusive education. Fadhilah Special School serves 17 special needs students consisting of 8 hearing-impaired students and 9 intellectually disabled students, but it only has 5 educators, some of whom do not yet possess special competencies or certification in Special Education (PLB). This research employs a descriptive qualitative approach with a case study method and data collection techniques through interviews, observations, and documentation. The findings indicate that HRM still faces several challenges, such as: a lack of professional teachers, irregular training, an evaluation system that is not yet based on measurable performance indicators, and limitations in learning support facilities. Nevertheless, there is an initiative from the school principal to improve the work climate and internal communication. The main focus of this research is to formulate a systematic human resource management strategy, starting from recruitment, training, mentoring, to evaluation, as a foundation for enhancing the quality of inclusive education. This research is expected to provide theoretical and practical contributions to strengthening human resource management in special needs schools that have similar characteristics
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".