Penerapan Model Problem Based Learning (PBL) Berbantuan Video Pembelajaran dan Quizizz Untuk Meningkatkan Hasil Belajar Siswa
Bibliographic record
Abstract
By implementing an effective learning model and packaging it using interesting technological media, it is also hoped that it will be able to improve student learning outcomes. As for the aim of this research is to determine the application of the model Problem Based Learning (PBL) assisted by learning videos and Quizizz to improve student learning outcomes. This research is classroom action research using test and observation methods. The subjects of this research were 25 students of class VII G of SMP Negeri 6 Kintamani. Data collection was carried out using test and observation methods during learning activities. The research results show that it was found that there was a significant increase in the average score of students' mathematics learning outcomes. Initially in the first cycle the average student score was 66,8 which is categorized as poor in Cycle I, to 77.2 in Cycle II which is already in the good category with an increase of 10.4. Furthermore, there were 60% of students who scored above the Learning Goal Achievement Criteria (KKTP) in Cycle I and 84% of students in Cycle II with an increase of 24%. Thus, student learning outcomes have improved by using the learning model Problem Based Learning (PBL) assisted by learning videos and Quizizz.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".