Sosialisasi Pengenalan English For Elementary bagi Anak-Anak Kelas 5 & 6 Madrasah Ibtidaiyah Ihya Ulumiddin Banjarmasin
Bibliographic record
Abstract
Mastery of English is crucial in the era of globalization, and English language learning should begin early in elementary school. This community service program aims to improve the understanding and English skills of 5th and 6th grade students at MI Ihya Ulumiddin Banjarmasin through a fun and interactive approach. The methods applied include interactive lectures, question and answer sessions, as well as the use of visual aids and English learning applications. The program also involves pretests and posttests to measure students' understanding before and after the program. The results show a significant improvement in students' understanding and English skills. Most students, who initially had low comprehension, demonstrated positive progress, especially in vocabulary and pronunciation. This program successfully increased students' interest, engagement, and confidence in speaking English. The activity-based approach proved effective in supporting more engaging and developmentally appropriate learning. Moving forward, it is important to continue developing teaching methods that align with students' characteristics to enhance their mastery of English.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".