Peningkatan Hasil Belajar Matematika Siswa Dengan Menggunakan Media Blok Dienes Pada Materi Operasi Penjumlahan Bilangan Cacah
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui adakah peningkatan hasil belajar matematika siswa yang diajarkan dengan menggunakan media Blok Dienes pada materi operasi penjumlahan bilangan cacah. Penelitian ini termasuk penelitian tindakan kelas (PTK) yang dilakukan dalam 2 siklus. Penelitian dilakukan di SDN Bokong 2 Kecamatan Takari, Kabupaten Kupang pada semester ganjil tahun ajaran 2022/2023. Subjek penelitiannya adalah siswa kelas III berjumlah 26 orang. Instrumen pengumulan data dalam penelitian ini antara lain lembar observasi, tes, dan dokumentasi. Hasil penelitian menunjukkan bahwa penggunaan media blok dienes dapat meningkatakan hasil belajar matematika siswa kelas III SDN Bokong 2 pada materi operasi penjumlahan bilangan cacah. Hal ini tergambar dari adanya peningkatan dan tercapainya kriteria minimal aktivitas guru, aktivitas siswa, dan hasil belajar matematika siswa kelas III SDN Bokong 2 setelah dibelajarkan dengan menggunakan media blok dienes. Persentase siswa yang mencapai KKM (70) di siklus 2 adalah 88,5% dengan rata-rata klasikal mencapai 84,3. Hasil ini meningkat dibandingkan dengan hasil belajar siklus 1 dimana persentase siswa yang mencapai KKM hanya 38% dengan rata-rata klasikal sebesar 69,2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.013 |
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".