Penerapan Problem Based Learnin (Pbl) Dalam Kurikulum Berbasis Kompetensi
Bibliographic record
Abstract
Salah satu metode pembelajaran yang dapat diterapkan dalam Kurikulum Berbasis Kompetensi adalah Problem Based Learning. Donald Woods McMaster merupakan orang yang pertama kali memperkenalkan istilah PBL, dan Fakultas Kedokteran Universitas McMaster, Ontario Kanada merupakan institusi kedokteran yang memperkenalkan PBL dalam dunia pendidikan. Ada empat prinsip penting dalam pembelajaran PBL, yaitu : pembelajaran merupakan suatu proses konstruktif. (Learning should be a constructive process), pembelajaran merupakan suatu proses yang dimotori oleh keinginan dari dalam diri sendiri (Learning should be a self directed process), pembelajaran merupakan suatu proses yang dimotori oleh keinginan dari dalam diri sendiri (Learning should be a self directed process) dan pembelajaran merupakan sesuatu yang diberikan kontekstual (Learning should be a contextual process). Salah satu metode yang digunakan dalam melaksanakan PBL adalah seven jumps tutorial. Metode ini terdiri dari tujuh langkah yang disusun sistematis sehingga diskusi mahasiswa tentang suatu masalah dapat berjalan dengan optimal dan mencapai tujuan baik sesuai karakteristik PBL Keywords : KBK, PBL, seven jumps
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".