Menguasai Bahasa Inggris dalam Satu Menit: Pengembangan Bahasa Inggris di STAIBA Kediri
Bibliographic record
Abstract
Due to the fact that students will eventually enter the workforce, English proficiency plays a crucial role in their education. The world of work requires workers who have the ability to speak English orally and in writing. Students of the PIAUD study program in the pesantren environment who carry out their daily routines in the pesantren environment must be ready to enter the world outside the pesantren with a variety of chosen jobs. With the routine of Islamic boarding school activities which are very minimal in the opportunity to learn English, students who graduate from boarding schools feel that they are lacking in mastering the international language. In addition, the lack of motivation also greatly affects them in learning English. Based on the problems faced by students in Islamic boarding schools, it is necessary to provide motivation and learn English that is able to change their mindset in learning English easily and quickly. The development of English in the "Mastering English in One Minutes" activity by presenting natives from Canada together with English language study students was carried out face to face after previously students received online classes 3 times in meetings. This development uses the question and answer method, where PIAUD study program students can directly ask and practice English natively and will provide a golden opportunity to study briefly and increase their motivation in learning English. So it is necessary to organize Community Service activities to improve students' English mastery in the Islamic boarding school environment
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 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".