MENINGKATKAN PRESTASI BELAJAR SISWA SMK MELALUI IMPLEMENTASI DISCOVERY LEARNING BASED ENGINE MANAGEMENT SYSTEM
Bibliographic record
Abstract
Sebagian besar siswa teknik ototronik SMKN Malang terlihat kurang bersemangat ketika kegiatan belajar yang ditunjukkan dengan beberapa siswa yang tidur di kelas, bermain handphone maupun laptop ketika pembelajaran berlangsung sehingga secara tidak langsung menyebabkan hasil belajar siswa menjadi rendah. Perbaikan kualitas pembelajaran sudah selayaknya diterapkan oleh pendidik dalam lingkungan SMK seperti menggunakan model pembelajaran interaktif. discovery learning model merupakan suatu metode pengajaran yang menitikberatkan pada aktifitas siswa dalam belajar. Dalam proses pembelajaran dengan metode ini, guru hanya bertindak sebagai pembimbing dan fasilitator yang mengarahkan siswa untuk menemukan konsep, dalil, prosedur, algoritma dan semacamnya. Tujuan dari penelitian ini adalh untuk mengetahui efektivitas discovery learning terhadap prestasi belajar siswa. Subjek penelitian adalah siswa teknik ototronik SMKN 6 Malang. Rancangan model menggunakan model Kemmis & Mc Taggart yang terdiri atas empat tahapan, yakni perencanaan, tindakan, pengamatan, dan refleksi. Hasil penelitian menunjukkan bahwa model pembelajaran discovery learning efektif dalam meningkatkan prestasi belajar siswa teknik ototronik SMKN 6 Malang.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".