The Relationship between Learning Effectiveness, Teacher Competence and Teachers Performance Madrasah Tsanawiyah at Serang, Banten, Indonesia
Bibliographic record
Abstract
<p>In this study, the problem is limited factors relating to the learning effectiveness and teacher competence in improving the teacher performance. Therefore, this study will try to get explanations from some main issues which include the learning effectiveness issue, and teacher competence to increase teacher performance in Madrasah Tsanawiyah at Serang, Banten, Indonesia. The goal in this research is to get facts about correlation and to see the levels of effectiveness learning and teacher competence as well as the teacher performance in Madrasah Tsanawiyah at Serang, Banten, Indonesia. This research is directed in describing and analyzing the data findings in depth are using the quantitative analysis methods of descriptive and inferential. Collecting data is using the instrument, about the instrument is used as a primary assessment way to collect the information about factors of effectiveness learning, teacher competence, and teacher performance. The samples are 150 Teacher in Madrasah Tsanawiyah at Serang, Banten, Indonesia. The results of this research is there is a relationship between learning effectiveness and teacher performance, there is a relationship between teacher competence and teacher performance, the results of this study can be concluded that increasing teacher performance can be carried out in the presence of learning effectiveness and teacher competence in the Madrasah Tsanawiyah, teachers who have the good performance seen from an process of learning effectiveness as well as the teacher competency.</p>
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".