The Improvement of Civic Education Instructional Quality Throught Make a Match Model Assisted Picture Card Media
Bibliographic record
Abstract
<p><em>Purpose of </em><em>research</em><em> to improve the quality of </em><em>C</em><em>ivic </em><em>Education </em><em>Instructional Quality Throught </em><em>Make a Match</em><em> Model Assisted </em><em>Picture Card Media</em><em>. </em><em>Research design</em><em> </em><em>used</em><em> </em><em>classroom action research</em><em>, it</em><em> </em><em>conducted</em><em> </em><em>of</em><em> </em><em>three</em><em> cycles with four stages</em><em>:</em><em> </em><em>planning</em><em>,</em><em> running,</em><em> </em><em>observ</em><em>ing</em><em>, and reflecti</em><em>ng</em><em>. The techniques data collection used </em><em>observation, </em><em>test</em><em>, documentation, interview and field notes</em><em>. The techniques of data analized used Qualitative and quantitative descriptive. The research findings showed: (1) the </em><em>skill</em><em> of teacher improved in every cycle. In cycle I, the score was </em><em>28</em><em> with good criteria. In cycle II, the score was </em><em>32</em><em> with good criteria. In cycle III, the score was </em><em>35</em><em> with very good criteria, (2) Students activity showed improvement in every cycle. In cycle I, the score was </em><em>21,7</em><em> with </em><em>enough</em><em> criteria In cycle II, the score was </em><em>25,13</em><em> with good criteria. In cycle III, the score was </em><em>28,21</em><em> with good criteria, (3) Students learning </em><em>outcome</em><em> showed improvement in every cycle with classical comprehension in cycle I </em><em>63</em><em>%, cycle II 76</em><em>,32</em><em>%, and cycle III 86</em><em>,84</em><em>%. Conclusion of the research is </em><em>make a match</em><em> Model Assisted </em><em>Picture Card Media</em><em> can improve the quality of civic </em><em>education </em><em>instructional.</em></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".