Application of The Contextual Teaching and Learning (CTL) Model in Improving The Quality of Balaghah Learning in MAN 3 Jombang
Bibliographic record
Abstract
In Indonesia, the problem that often arises in learning is that students' understanding of the material is still low, as evidenced by student learning outcomes that are still low. This is certainly not caused by the common knowledge of students alone. Still, many factors that influence one of them are the learning process that is less qualified, human resources in the form of unprofessional teachers so that students' abilities will also be low. If this continues, the output of human resources resulting from learning in Indonesia will not be able to compete with countries that are both developing. This study aims to reveal in more depth the application of the contextual teaching and learning (CTL) learning model in improving the quality of Balaghah learning at MAN 3 Jombang, with the focus of the discussion as follows 1) Planning of Contextual Teaching and Learning (CTL) models in improving the quality of Balaghah learning at MAN 3 Jombang, (2) the quality of Balaghah learning as the output of the application of the Contextual Teaching and Learning (CTL) model at MAN 3 Jombang. This research uses a qualitative approach, with the type of case study research in schools. Data collection was done by interview, observation, and documentation. Data analysis was done through data reduction, presentation, and conclusion. The results of this study are 1) Planning the Contextual Teaching and Learning (CTL) Model, there are three stages; first, the introduction is to see the suitability of essential competencies with methods, materials, models, media, and learning resources. Second, implementation is the student centre learning process through several stages of learning. Third, the teacher evaluates the students. 2) The quality of Balaghah learning as the output of applying the Contextual Teaching and Learning (CTL) model at MAN 3 Jombang is related to teacher learning behaviour, student learning behaviour, impact, learning climate, learning materials, and learning media
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".