The effect of academic and pedagogic competences on basic teaching skills of mathematics teacher candidates in micro teaching
Bibliographic record
Abstract
Keterampilan mengajar merupakan salah satu keterampilan dasar yang harus dikuasai oleh seorang guru. Oleh karenanya, mahasiswa calon guru perlu terus menerus melatih diri untuk mengembangkan keterampilan mengajarnya. Salah satu cara yang diusahakan adalah melalui pengajaran mikro pada mata kuliah PSAP (Perencanaan, Strategi, Asesmen, Pembelajaran) Matematika. Tujuan penelitian ini adalah untuk mengetahui kontribusi dari kemampuan akademik dan pedagogik mahasiswa calon guru terhadap keterampilan dasar mengajar mereka dalam pengajaran mikro. Subjek penelitian terdiri dari 56 mahasiswa prodi Pendidikan Matematika Universitas Pelita Harapan angkatan 2017. Penelitian ini merupakan penelitian korelasional dan analisis regresi linear sederhana. Instrumen yang digunakan dalam pengumpulan data meliputi rubrik pengajaran mikro, arsip nilai mahasiswa, dan kuesioner. Hasil penelitian ini adalah: (1) terdapat korelasi yang signifikan antara kemampuan akademik dan keterampilan dasar mengajar; (2) terdapat pengaruh yang signifikan dari kemampuan akademik terhadap keterampilan dasar mengajar; (3) terdapat korelasi yang signifikan antara kemampuan pedagogik dan keterampilan dasar mengajar; serta (4) terdapat pengaruh yang signifikan dari kemampuan pedagogik terhadap keterampilan dasar mengajar. Ini berarti, mata kuliah pedagogi dan konten dasar matematika yang diberikan sudah sesuai dengan kebutuhan mahasiswa.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".