PENINGKATKAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN STRATEGI PROBLEM BASED LEARNING DI SMK NEGERI 1 BUNGO
Bibliographic record
Abstract
The background of this research is the low learning outcomes of mathematics subjects class XII Ak Muara Bungo. The causative factor is that learning is still conventional, there is no student involvement in the learning process, not using Problem Based Learning strategies in the learning process. This research is a class action research with qualitative and quantitative approaches. The subjects of the study were class XII students of Ak Muara Bungo which numbered 30 students. In its implementation, this study consists of 2 cycles carried out by researchers. Each cycle consists of 4 stages, namely action planning activities, action implementation, observation, and reflection on each cycle. The data of this study were collected based on observations, learning outcomes tests, and documentation. The results showed that 1) the application of Problem Based Learning strategies can improve the results of teacher and student activities. Teacher activities in cycle I by 81% (good category) in cycle II increased to 91% (very good category). Student activities in cycle I by 84% (good category) in cycle II increased to 90% (very good category). 2) Improvement in learning outcomes that achieve grades above KKM (65) with a pre-cycle percentage of 57%, cycle I 60%, and cycle II 76%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".