Efektivitas Pembelajaran Kooperatif Pelatihan Dasar Calon Pegawai Negeri Sipil di Balai Diklat Aparatur Kementerian Kelautan dan Perikanan
Bibliographic record
Abstract
This study aimed to determine the effectiveness of cooperative learning methods in civil servant candidate training batch 7 at Apparatus Training Center, Ministry of Maritime Affairs and Fisheries This research were classroom action research with a quantitative approach. Data collection method used questionnaire, assignment sheet, interview and observation. This research conducted by comparing learning effectiveness in two different class namely experiment class that implement cooperative learning and control class that not implement cooperative learning. Learning effectiveness was assesed using two indicators namely activeness and learning result. Data analysis conducted by comparing learning result from experiment class and control class. The result of data processing shows that the average activeness score for the control class is 66.8% and the experimental class is 85.6%. The average learning result of the control class is 82.03 and the experimental class is 91.2. From these data, there is a percentage increase in the active score of participants from the control class compared to the experimental class 28.1% and the percentage increase in learning outcomes 11.2%. It can be conlused that cooperative learning method can increase the learning effectiveness of civil servant candidate training batch 7. Cooperative learning method can be used as alternative learning method at civil servant candidate training.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".