FAKTOR DETERMINAN YANG DAPAT MEMPENGARUHI KINERJA GURU PAUD (STUDI SURVEY DI KORWIL BIDANG PENDIDIKAN KECAMATAN BUNGBULANG KABUPATEN GARUT)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh kepemimpinan transformasional, kompetensi pedagogik, motivasi kerja terhadap kinerja guru Pendidikan Anak Usia Dini (PAUD) di Koordinator Wilayah Pendidikan, dengan menggunakan objek penelitian PAUD di Kecamatan Bungbulang Kabupaten Garut. Sampel penelitian ini adalah guru PAUD sebanyak 75 responden. Metode penelitian menggunakan pendekatan kuantitatif, instrumen penelitian menggunakan angket dan analisis regresi berganda. Hasil penelitian analisis deskriptif, pendapat responden tentang kepemimpinan transformasional, kompetensi pedagogik, motivasi kerja terhadap kinerja guru PAUD rata-rata masih tergolong kategori cukup baik, sedangkan hasil analisis verifikasi menunjukkan secara parsial atau simultan kepemimpinan transformasional, kompetensi pedagogik dan kerja motivasi. berpengaruh signifikan terhadap kinerja guru PAUD, dengan koefisien korelasi (r) sebesar 0,860, terdapat hubungan yang sangat kuat antara kepemimpinan transformasional, kompetensi pedagogik, motivasi kerja dan kinerja guru PAUD, sedangkan R2 sebesar 74%, artinya PAUD kinerja guru masih dipengaruhi oleh variabel yang tidak dijadikan model penelitian ini sebesar 26%. Kepemimpinan transformasional harus lebih ditingkatkan dengan mengembangkan guru agar memiliki kompetensi kepribadian, profesional dan sosial. Kompetensi pedagogik dinilai baik.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".