STRATEGI PEMBELAJARAN AKTIF: PEMANFAATAN METODE DRILL, TEAMWORK, DAN ROLE PLAY DALAM PEMBELAJARAN PENDIDIKAN AGAMA ISLAM
Bibliographic record
Abstract
Islamic Religious Education (PAI) learning has an important role in shaping the character and morality of Muslim individuals. To achieve this goal, an effective learning approach is needed. Effective learning is supported by appropriate learning methods and their impact on student learning outcomes. Some learning methods that can be used are drill, teamwork and role play methods. The drill method is an approach used to strengthen students' understanding of Islamic religious concepts through repeated practice. The teamwork method in PAI learning involves collaboration between students in groups to understand and apply Islamic religious concepts. The role play method in PAI learning involves role simulations where students play certain roles in situations related to the teachings of the Islamic religion. The research results show that the integration of these three methods improves the implementation of learning by creating a more interactive and interesting learning experience, which in turn deepens understanding and strengthens retention of lesson material. This research provides a new contribution to the PAI education literature by showing the potential of interactive learning methods in improving the quality of PAI learning. Carrying out the learning process by determining appropriate methods aims to help students internalize and remember religious information better. Keywords: Drill method, teamwork, role play, PAI learning
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".