Upaya Meningkatkan Motivasi Belajar IPA melalui Model Pembelajaran Quantum Teaching di Kelas V SDI Tabene
Bibliographic record
Abstract
Improving students' learning motivation in Natural Sciences (IPA) in fifth grade students of SDI Tabene through the application of the Quantum Teaching learning model. The Quantum Teaching learning model, with the TANDUR principle (Grow, Experience, Name, Demonstrate, Repeat, Celebrate), is designed to create a fun and relevant learning environment to students' experiences. This Classroom Action Research was carried out in two cycles, each consisting of planning, implementation, observation, and reflection. The subjects of the study were 32 fifth grade students of SDI Tabene. Data were collected through observation, tests, and documentation. The results showed an increase in students' learning motivation. In cycle I, the average student learning motivation was 60.7%, which was classified as sufficient. After improvements were made in cycle II by optimizing each stage of TANDUR, the average student learning motivation increased to 82.1%, classified as very good. This increase was seen from students' higher enthusiasm, active participation in discussions, and increased curiosity about science materials. It can be concluded that the application of the Quantum Teaching learning model is effective in improving students' learning motivation in fifth grade students of SDI Tabene.
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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) | 0.006 | 0.001 |
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".