Headmaster Instructional Leadership and Organizational Learning on the Quality of Madrasah and the Quality of Graduates the State Madrasah Aliyah at Jakarta Capital Region
Bibliographic record
Abstract
The purpose of this study is to look closely aspects of instructional leadership, and organizational learning affect the quality of madrasah in improving the quality of graduate the state madrasah aliyah. The experiment was conducted using a quantitative approach with descriptive and inferential methods, in inferential methods used correlation analysis and regression analysis. The process is first conducted analysis of data validity and reliability of data as well as test for normality using the Kolmogorov-Smirnov Test. The study population is the overall teacher the State Madrasah Aliyah at Jakarta Capital Region. The study sample size of 150 teachers. The collecting data about the instrument with research using Likert scale, to obtain data on instructional leadership, organizational learning, quality of madrassah and the quality of graduates. The results of research known that headmaster instructional leadership has a strong and positive relationship with the quality of the madrasah, the quality of graduates, organizational Learnings have strong relationships and positive impact on the quality of madrasah. The quality of graduates and have a reciprocal relationship with a high instructional leadership. It can be concluded that an increasing in the quality of madrasah and the quality of graduates at the school can be done with an increase in instructional leadership and organization of learning in the madrasah. Thus improvement instructional leadership and organizational learning have a positive influence on the improvement of the quality of madrasah and the achievement of the quality of graduates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".