MANAJEMEN PEMBELAJARAN PARTISIPATIF BERBASIS PROJECT BASED LEARNING PADA MATERI FISIKA HUKUM PASCAL
Bibliographic record
Abstract
This study aims to provide a clear and comprehensive picture to students of class XI IPA 2 SMA Negeri 3 Tondano about learning management which includes planning, organizing, implementing, and evaluating participatory learning based on project-based learning on pascal’s law material in physics subjects. The method used in this research is mix method with concurrent model which is a combination of two methods, especially qualitative and quantitative. The structure of this research uses qualitative methods while quantitative methods are used to assist qualitative information. The results of this study are starting from the planning stage including the syllabus and lesson plans adapted to the strategy. The next stage is organizing which result in the function and role of management components. The implementation stage is in accordance with the syntax of participatory learning. This implementation stage shows that the implementation of management functions in participatory learning based on project-based learning on pascal’s law physics material for SMA N 3 Tondano class XI IPA 2 students can shape students’ cognitive, affective and psychomotor competencies through making learning projects. Based on the results of the study, it is concluded that planning, organizing, implementing and evaluating participatory learning based on project-based learning is well implemented as seen from the cognitive, affective and psychomotor assessment rubrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".