The Improvement of Civic Education Instructional Quality Throught Make a Match Model Assisted Picture Card Media
Bibliographic record
Abstract
Purpose of research to improve the quality of Civic Education Instructional Quality Throught Make a Match Model Assisted Picture Card Media. Research design used classroom action research, it conducted of three cycles with four stages: planning, running, observing, and reflecting. The techniques data collection used observation, test, documentation, interview and field notes. The techniques of data analized used Qualitative and quantitative descriptive. The research findings showed: (1) the skill of teacher improved in every cycle. In cycle I, the score was 28 with good criteria. In cycle II, the score was 32 with good criteria. In cycle III, the score was 35 with very good criteria, (2) Students activity showed improvement in every cycle. In cycle I, the score was 21,7 with enough criteria In cycle II, the score was 25,13 with good criteria. In cycle III, the score was 28,21 with good criteria, (3) Students learning outcome showed improvement in every cycle with classical comprehension in cycle I 63%, cycle II 76,32%, and cycle III 86,84%. Conclusion of the research is make a match Model Assisted Picture Card Media can improve the quality of civic education instructional.
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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 | 0.009 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".