Implementation of Flipped Classroom as Student-Centered Learning Implementation in Awatara Learning of 7 Grade At SMP Negeri Satap 2 Kintamani
Bibliographic record
Abstract
Talking about learning and teaching certainly cannot be separated from how skilled an educator is in developing learning strategies. This also applies to all subjects taught, one of which is Hinduism. Learning Hinduism aims to build morals and virtuous behavior through the guidelines of the holy book, namely the Vedas, so this causes many educators in the field of Hinduism to only focus on conveying teachings from the Vedic scriptures, especially the story of Avatar by delivering material through the lecture method. without paying attention to the interests of their students. Even though in this era of globalization, children are often easily influenced by foreign cultures so interest in exploring their own beliefs is reduced, and each student has a variety of learning styles. The ability of educators to design learning strategies greatly influences students' interest in Hinduism lessons and students can internalize and deepen the moral values that are learned through the learning process they go through, one of which is by implementing flipped classrooms as an implementation of student-centered learning in Avatara learning for learning experiences which is more meaningful. This research is qualitative in nature with data collection methods in the form of participant observation. The results obtained are that through the flipped classroom students can train their creative thinking skills well and can create innovations based on moral values obtained from Avatara learning. The conclusion from this study is that flipped classes can be a solution to teaching strategies for learning Avatara material as the application of student learning centered for a meaningful learning process amid the demands of teachers who must understand the diverse student learning styles.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".