Multicultural Problem-Based Learning: Strategies for Improving Religious Moderation in Elementary Schools
Bibliographic record
Abstract
This study examines teachers’ perceptions of the relevance and effectiveness of Multicultural Problem-Based Learning (MPBL) in strengthening religious moderation attitudes among Elementary School and Madrasah Ibtidaiyah students in Kuningan Regency, Indonesia. A quantitative survey approach was employed using a questionnaire developed from key dimensions of religious moderation, namely national commitment, religious tolerance, appreciation of religion-based local culture, and religion-based anti-violence, as well as indicators of the importance of MPBL in primary education. The results indicate that teachers overwhelmingly perceive MPBL as a highly relevant and effective learning approach. MPBL is considered capable of strengthening students’ character in appreciating diversity, reducing negative stereotypes and prejudice, and fostering critical, collaborative, and solution-oriented thinking skills. Teachers also agree that this approach supports the internalization of tolerance, respect for different religious beliefs, and appreciation of local cultural wisdom rooted in religious values. However, the findings reveal that students’ deeper understanding of the universal principle that all religions teach peace has not been fully internalized, indicating a gap between cognitive understanding and affective–behavioral development.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".