The Madrasah Leadership, Teacher Performance and Learning Culture to Improve Quality at Madrasah Tsanawiyah Negeri Jakarta of South
Bibliographic record
Abstract
The aim of this study was to analyze the impact of the madrasah leadership, teacher performance and learning culture with madrasah quality. The research objectives to be achieved are: Identify the level of the madrasah leadership, teachers performance, learning culture and the quality of the madrasah; Identify the relationship of the madrasah leadership with madrasah quality; Identify the relationship of the teacher performance with madrasah quality; Identify the relationship of the learning culture with madrasah quality; Identify the relationship model of the of madrasah leadership, teacher performance and learning culture with madrasah quality. The design of this study is quantitative research methods with methods of descriptive and inferential. This study applies a survey method to collect quantitative data through questionnaires. The findings of this study that there is a correlation between madrasah leadership are quality madrasah. There is a relationship to the teacher performance with quality of madrasah. There is a relationship to the learning culture with quality of madrasah. There is a relationship model of madrasah leadership, teacher performance and learning culture of the quality of madrasah. Therefore, based on the results of this study to produce high madrasah quality can be done by improving of the leadership of madrasah, teacher performance and learning culture in madrasah. If the leadership of madrasah, teacher performance and learning culture is low, it will result a lower quality in madrasah.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".