EVALUASI IMPLEMENTASI PROGRAM PELATIHAN PENYULUH PERTANIAN DI BALAI BESAR PELATIHAN PERTANIAN (BBPP) LEMBANG
Bibliographic record
Abstract
Evaluasi program dan berkelanjutan perlu dijalankan mengacu pada standar perbandingan yang telah ditetapkan untuk mencapai perbaikan yang diperlukan. Penelitian ini berfokus pada evaluasi menyeluruh program pelatihan dari perencanaan hingga hasil akhir, menerapkan model evaluasi yang dikembangkan oleh Daniel L. Stufflebeam. Model ini memiliki empat dimensi utama: context, input, process, product. Dimensi context membantu merencanakan keputusan program, mengevaluasi tujuan yang ingin dicapai, dan mengidentifikasi tujuan yang relevan. Dimensi input dilakukan untuk mengidentifikasi alokasi sumber daya yang dibutuhkan dan dukungan dari pihak eksternal. Dimensi process mendukung implementasi keputusan, pemantauan, pengendalian, dan perbaikan prosedur pelaksanaan. Sementara dimensi product dirancang mengukur dan menginterpretasikan pencapaian tujuan akhir program. Penelitian ini menggunakan pendekatan kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan studi dokumen. Data dianalisis mengikuti model analisis Miles dan Huberman, termasuk tahapan pengumpulan data, reduksi, penyajian, dan penarikan kesimpulan. Hasil evaluasi pada dimensi context melibatkan analisis hukum, kebutuhan, tujuan, dan relevansi kurikulum. Evaluasi dimensi input mencakup aspek penyelenggara, peserta, instruktur, materi, metode, media, infrastruktur, dan pendanaan. Dimensi process dievaluasi melalui jadwal, kinerja penyelenggara dan instruktur, partisipasi peserta, serta mekanisme evaluasi yang diterapkan. Pada dimensi product, indikator mencakup penguasaan materi dan tingkat kelulusan peserta sebagai ukuran keberhasilan mencapai tujuan program. Evaluasi ini menjadi dasar untuk meningkatkan kualitas program pelatihan, terutama dalam konteks pertanian. Rekomendasi termasuk pembaruan materi pelatihan dan peningkatan pemanfaatan sumber daya lahan, diharapkan dapat menghasilkan perbaikan signifikan dalam kualitas program dan kompetensi peserta. Evaluasi ini memegang peran penting dalam pengembangan berkelanjutan dan peningkatan efektivitas program pelatihan. Program and ongoing evaluations need to be carried out against established standards of comparison in order to achieve the necessary improvements. This research focuses on a comprehensive evaluation of the training program from planning to final outcomes, applying the evaluation model developed by Daniel L. Stufflebeam. This model has four main dimensions: context, input, process, product. The context dimension helps to plan program decisions, evaluate the goals to be achieved, and identify relevant objectives. The input dimension is used to identify the required resource allocation and external support. The process dimension supports decision implementation, monitoring, control, and improvement of implementation procedures. While the product dimension is designed to measure and interpret the achievement of the program's final objectives. This research used a qualitative approach with data collection techniques through observation, interviews, and document studies. Data were analyzed following the Miles and Huberman analysis model, including the stages of data collection, reduction, presentation, and conclusion drawing. The evaluation results on the context dimension involved analyzing the law, needs, objectives, and relevance of the curriculum. The input dimension evaluation included aspects of organizers, participants, instructors, materials, methods, media, infrastructure, and funding. The process dimension is evaluated through the schedule, performance of organizers and instructors, participant participation, and the evaluation mechanism applied. In the product dimension, indicators include mastery of the material and the graduation rate of participants as a measure of success in achieving program objectives. This evaluation provides a basis for improving the quality of training programs, especially in the context of agriculture. Recommendations, including updates to training materials and improved utilization of land resources, are expected to result in significant improvements in program quality and participant competencies. This evaluation plays an important role in the continuous development and improvement of training program effectiveness.
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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 | 0.011 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".