PENERAPAN METODE QUANTUM LEARNING SEBAGAI UPAYA MEWUJUDKAN PROSES STUDENT CENTERED LEARNING MATA KULIAH TEORI ORGANISASI
Bibliographic record
Abstract
Tujuan penelitian tindakan kelas (PTK) ini adalah untuk mewujudkan proses Student Centered Learningmahasiswa untuk mata kuliah TO kelas C semester genap tahun ajaran 2015-2016 dengan menggunakan metodequantum learning. Metode quantum learning menggunakan gabungan antara tugas diskusi kelompok denganposisi duduk yang diatur melingkar dan adanya music yang mengiringi. Jawaban dari rumusan masalah adalahmetode quantum learning cukup berperan dalam pelaksanaan student centered learning,meskipun masih perluperbaikan untuk beberapa variabel khususnya prioritas lebih dahulu kepada variabel 2 (kesiapan mahasiswasebelum mengikuti perkuliahan) & 5 (kemampuan mahasiswa dalam menjawab pertanyaan). Pelaksanaanstudent centered learning dapat dilihat dari rata‖ nilai kuis dalam kisaran nilai 70 serta terdapat peningkatannilai UTS ke UAS yang cukup besar (dari 54 ke 86).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".