Penerapan Metode Permainan Papan Memori untuk Meningkatkan Keaktifan Belajar Siswa pada Mata Pelajaran Sejarah Kelas X di SMK Arnoldus Betun
Bibliographic record
Abstract
This researcher aims to improve student learning activity in history subjects of class X SMK Arnoldus Betun through the application of the memory board game method. The formulation of the problem raised in this study is how to apply the memory board game method to improve student learning activity in history subjects and what is the impact of applying the memory board game method to improve student learning activity in history subjects. This researcher uses a classroom action research (CAR) method consisting of three cycles. Each cycle includes the stages of planning, implementation, observation, and reflection. The data collection instruments used are observation sheets and student learning interest questionnaires. The results of the study showed a significant increase in student learning interest after the application of the memory board game method. In the second cycle, the average student learning activity was at 60%, and increased gradually to reach 86% in the third cycle. The discussion of these results shows that the memory board game method is not only able to increase student learning activity, but also facilitates collaboration, active participation, and student creativity in the history learning process. Thus, it can be concluded that the memory board game method is effective in increasing the active learning of history among 10th-grade students at Arnoldus Betun Vocational School.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".