Implementasi Gap Analysis untuk Evaluasi Kinerja Dosen Berdasarkan Sasaran Mutu
Bibliographic record
Abstract
Dosen merupakan pendidik yang salah satu tugasnya mentransformasikan dan menyebarluaskan ilmu pengetahuan, sehingga kinerja dosen dalam proses belajar harus bagus. Evaluasi dosen di STMIK AKAKOM dilakukan setiap semester sekali untuk melihat nilai kemampuan pedagogik, kompetensi dosen, ketersediaan sarana proses belajar mengajar dan kelengkapan administrasi dalam proses belajar mengajar. Evaluasi dilakukan oleh mahasiswa dan Tim Penjaminan Mutu Program Studi (TPMP). Standar-standar penilaian yang seharusnya dicapai salah satunya tentang kinerja dosen dalam proses belajar mengajar tertuang dalam dokumen sasaran mutu institusi. Hasil evaluasi diharapkan sesuai dengan sasaran mutu di STMIK AKAKOM. Selama ini hasil evaluasi tidak pernah diolah. Penelitian ini mengolah data hasil evaluasi dengan mencocokkan nilai yang ada di sasaran mutu dengan metode gap analysis. Metode ini digunakan karena dapat melihat kesenjangan antara kinerja dosen dengan standar yang sudah ditetapkan. Hasil penelitian menunjukkan bahwa Gap analysis dapat membantu membuat perangkingan pada dosen di STMIK AKAKOM. Hasil ini dapat digunakan oleh pimpinan dalam mengambil keputusan dalam pemberian reward bagi dosen berprestasi dan memberi binaan terhadap dosen yang rankingnya rendah.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".