Improving Student Learning Outcomes through the Application of Cooperative Learning Models (Student Teams Achievement Divisions Type) in Islamic Religious Education Subjects, (Case Study: Class IV SDN 1 Kraksaan, Probolinggo Regency)
Bibliographic record
Abstract
In connection with this vision, a set of principles for implementing education has been established to be the basis for implementing education reform. One of these principles is that education is held as a process of civilizing and empowering students that lasts throughout life (Rusman, 2010: 3). Islamic Religious Education is a religious education which is a condition of moral and commendable behavior. With the establishment of Islamic Religious Education is expected to be able to sustain the development of good character of students so as to produce educational products that are of perfect character. Further discussing some of the notions of Islamic Religious Education which include Islamic Religious Education is interpreted as a conscious and planned effort in preparing students to recognize, understand, appreciate to believe, have faith, and have good morals in practicing the teachings of Islam from its main source al- Qur'an and Hadith, through the activities of guidance, teaching, training, and the use of experience. Accompanied by guidance to respect adherents of other religions in relation to harmony between religious communities in the community to realize the unity and integrity of the nation
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".