Pemupukan Kemahiran Insaniah Melalui Kursus Bina Insan Guru dalam Kalangan Siswa Guru Institut Pendidikan Guru
Bibliographic record
Abstract
Kertas konsep ini membincangkan strategi pemupukan pelbagai kemahiran insaniah melalui program BIG di Institut Pendidikan Guru (IPG) yang menjadi medium untuk melahirkan seorang bakal guru yang benar-benar komited, cemerlang dan serba boleh apabila berkhidmat sebagai guru kelak.Siswa guru kesemua opsyen di IPG perlu mengikuti program Bina Insan Guru (BIG) berdasarkan semester tertentu untuk enam tahap.Program BIG melibatkan ativiti perkhemahan, keyakinan diri ketika melakukan aktiviti air, keyakinan diri dalam melakukan aktiviti kembara, membina jaringan hubungan sosial yang baik serta penerapan nilai-nilai murni menerusi aktiviti Latihan Dalam Kumpulan (LDK).Antara lain, kemahiran insaniah yang dipupuk melalui program BIG ini ialah cergas dan patriotik dalam fasa pertama, akauntabiliti, amanah, kreativiti dan inovatif dalam fasa kedua, etiket sosial, protokol, kesantunan berbahasa, kemahiran mendengar secara efektif, tertib di meja makan, etika berpakaian dalam fasa ketiga, kemahiran berfikir aras tinggi ketika membuat perancangan dan pengendalian dalam fasa keempat, ciri-ciri kepimpinan, pengurusan sekolah, iklim sekolah, infrastruktur dan infostruktur kurikulum di sekolah dalam fasa kelima dan meningkatkan profesionalisme kendiri secara berterusan dalam fasa keenam.Metodologi kajian ini bersifat kualitatif dengan kaedah pengumpulan data dilakukan secara analisis kandungan dan permerhatian ikut serta.Pengumpulan data secara analisis kandungan dilakukan dengan menganalisis dokumen Panduan Program Ijazah Sarjana Muda Perguruan (PISMP) di IPG, manakala secara pemerhatian ikut serta pengkaji menyertai dan memerhati kegiatan yang dijalankan oleh siswa guru PISMP di IPG.Hasil pemerhatian atau kajian utama memaparkan keberkesanan program BIG yang dilaksanakan dalam memupuk kemahiran insaniah dalam diri siswa guru di IPG.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".