Bibliographic record
Abstract
Buku ini ditulis berdasarkan pada pengalaman kami memberikan workshop desain instruksional di kalangan IAIN dan STAIN. Semua materi dalam buku ini dirangkum dari bahan workshop pendidikan tinggi yang dilaksanakan oleh Center for University Teaching & Learning (CUTL) McGill University Kanada. Buku ini ditulis oleh sebuah tim yang terdiri dari enam orang, 3 orang dari UIN Sunan Kalijaga, 1 orang dari Banjarmasin dan 1 orang lagi dari Bandung. Materi yang dirangkum dalam buku ini terbagi dalam empat bagian: desain materi perkuliahan, tujuan pembelajaran, strategi pembelajaran dan evaluasi pembelajaran. Semua materi disampaikan secara berurutan mulai dari pendekatan teoritis, dilanjutkan dengan beberapa aktivitas serta contoh-contoh kegiatan. Workshopya sendiri menggunakan pendekatan andragogi. Hal ini dilakukan agar pola pemblejaran di kelas dapat berlangsungsecara partisipatif, variatif dan interaktif seusia dengan pengalaman masing-masing peserta. Berbagai pengalaman yang digali dari para peserta akan dijadikan sumber inspirasi ketika para peserta diajak untuk berdiskusi berdasarkan pada pengalaman.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".