Pelatihan Penggunaan Aplikasi Duolinggo dan Hellotalk sebagai Media Pembelajaran Bahasa Inggris di SMA Leona Desa Lotas di Daerah Terpencil di Perbatasan NKRI-RDTL
Bibliographic record
Abstract
The development of mobile technology provides new opportunities for independent English learning through digital applications. This community service research aims to implement training on the use of Duolingo and HelloTalk English applications at Leona High School, Lotas Village, Rinhat District. The training was conducted in two weeks, combining lecture methods, demonstrations, direct practice, group discussions, and evaluation through pre-tests and post-tests. The results showed an increase in the average score from 55 to 78 (p < 0.001), with 72% of participants consistently studying daily during the training. In addition to improving basic English skills (vocabulary, reading comprehension, and sentence structure), the training also succeeded in building independent learning habits and digital literacy. Obstacles emerged, especially internet access and limited premium features on some applications. Advanced programs (advanced grammar & speaking), mentoring, and provision of digital infrastructure support are recommended
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".