Model Pembelajaran Tematik Terpadu Berbasis Project Based Learning (PjBL) di Sekolah Dasar
Bibliographic record
Abstract
The purpose of this study is to describe the planning and implementation of integrated thematic learning based on Project Based Learning/PjBL in elementary schools. This research uses a qualitative descriptive approach. The data generated are in the form of a description of the PjBL-based integrated thematic learning planning model and a description of the PjBL-based integrated thematic learning implementation model in Blitar City Elementary School. The subject of this research is a teacher who supports integrated thematic learning in three elementary schools in the City of Blitar. The findings of the study indicate that PjBL-based integrated learning in Border Elementary Schools has an average percentage of 98.3%, Outer Elementary Schools of 97.2% and Urban Elementary Schools of 98.6%; while the implementation of the PjBL-based integrated thematic learning model in Border Elementary Schools obtained an average percentage of 84.4%, Outer Elementary Schools of 71.4%, and Urban Elementary Schools of 88.6%. It was concluded that elementary school teachers in the City of Blitar in implementing integrated thematic learning based on PjBL obtained an average of 89.43%. Thus, it is concluded that teachers have very good abilities in implementing PjBL-based integrated thematic learning in elementary schools
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".