Pengaruh Penggunaan Teknologi dalam Pembelajaran Bahasa Indonesia terhadap Kemampuan Menulis Pada Siswa Kelas III SDI Betun Kota
Bibliographic record
Abstract
This study aims to determine the effect of technology use in Indonesian language learning on students' writing skills. The research method used was descriptive quantitative. This study was conducted at SDI Betun Kota, Wehali Village, Malaka Tengah District, Malaka Regency. The data collection technique used was a test, and the analysis technique used was a statistical t-test. The results showed that 18 students, or 44.44%, obtained high scores in writing skills, while 16.67% scored low in learning outcomes. This is evidenced by the results of the significance test, namely t-test of 14.52, which is greater than t-table at the 5% significance level. N = 18 (2.14), or equal to t-test > t-table, or 16.67 > 2.14. Based on the research results above, t-test is greater than t-table, with t-test = 16.67 and t-table = 2.14. Therefore, Ho is rejected and Ha is accepted. Thus, it can be concluded that technology use influences students' writing skills in Indonesian language learning in grade III of SDI Betun Kota.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".