Peningkatan Motivasi dan Hasil Belajar Matematika Peserta Didik SMP Menggunakan LKPD Berbantuan Whatsapp Group Berdasarkan Gender
Bibliographic record
Abstract
Penelitian ini bertujuan untuk meningkatkan motivasi dan hasil belajar matematika peserta didik SMP menggunakan LKPD berbantuan WhatsApp Group berdasarkan gender. Metode penelitian menggunakan tindakan kelas tiga siklus. Pengumpulan data menggunakan tes, angket, lembar observasi, dan wawancara. Teknik analisis data deskriptif kualitatif dan uji one sample t-test. Hasil penelitian sebelum tindakan didapatkan rata-rata motivasi peserta didik laki-laki 22.73 dan perempuan 25.41. Setelah diberikan tindakan 3 siklus didapatkan nilai rata-rata peserta didik laki-laki 24,47, 25,13 dan 26,33. Nilai rata-rata peserta didik perempuan 26,65, 27,41 dan 29,18. Hasil uji t pada 3 siklus didapatkan nilai berturut-turut 0,029, 0,035, dan 0,03. Hasil ini menunjukkan bahwa motivasi belajar peserta didik perempuan lebih tinggi daripada laki-laki. Sedangkan hasil belajar sebelum diberikan tindakan didapatkan nilai rata-rata peserta didik laki-laki 42,00 dan perempuan 60,59. Setelah diberikan tindakan 3 siklus didapatkan nilai rata-rata peserta didik laki-laki 58,33, 70,67, dan 78,67. Nilai rata-rata peserta didik perempuan 71,18, 83,53, dan 88,24. Hasil Uji t pada siklus 1 dan 2 berturut-turut 0,042, dan 0,035. Sedangkan pada siklus 3 tidak ada perbedaan hasil belajar peserta didik berdasarkan gender. Berdasarkan analisis data maka penggunaan LKPD berbantuan WhatsApp Group kelas VIIIA efektif dalam meningkatkan motivasi dan hasil belajar matematika yang didasarkan pada perbedaan gender. Kata Kunci: Gender, Hasil Belajar, Motivasi Belajar, Pembelajaran Matematika
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 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".