Designing English Instructions for Islamic Settings: A Need Analysis in Indonesian Pesantren
Bibliographic record
Abstract
Many pesantren in Indonesia have been significantly transformed to adapt to the demands of the times, by providing English instructions in their curriculum. Although existing literature has discussed the needs of pesantren students in different parts of Indonesia, little is known about the needs in East Lombok, West Nusa Tenggara, a rural part of Indonesia. A modern pesantren, Darul Muttaqien NWDI Perian, had just been transformed to provide an English instruction for about two years while Nurul Azhar Sukadana, a khalafi pesantren, had included English instructions for quite a while. Nonetheless, both of the management did not yet have a clear curriculum that met the students’ needs. Therefore, it is crucial that this study investigate the needs of these pesantren, both from students and relevant parties’ expectations. Needs analysis in this context was the first phase of the R&D method. The data was collected by distributing questionnaires to 73 students and conducting a semi-structured interview with five teachers, three stakeholders, two parents, and community representatives. The findings revealed that English instructions should be designed to (a) support the improvement of students’ language skills, particularly speaking, (b) promote their ability to deliver speeches for religious purposes, and (c) prepare them for their future goals. Hence, in addition to Islamic topics and pesantren-related vocabulary, more modern and contextual materials should be included to widen their knowledge which in turn help them achieve their goals. Thus, these results can become the groundwork for developing the English curriculum in Pesantren in accordance with the students’ needs.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".