Developing Genre-based English Worksheet for Vocational Students by Using 4-D Development Model to Improve Writing Skill
Bibliographic record
Abstract
The research aims to develop genre-based English worksheets for the Basic English course and assess the validity of student worksheets of vocational students. This study explores worksheet development using a 4-D development model with stages of definition, design, development, and dissemination. This study employed research and development design. The instrument used was an observation by analyzing student needs involving vocational students at Akademi Komunitas Negeri Putra Sang Fajar Blitar, Universitas Sumatera Utara, Politeknik Negeri Medan, and Universitas Bengkulu. In define stage, this writing worksheets are emphasized with a reflective thinking approach so that students understand the material presented without obstacles. The independent learning curriculum is the basis for enriching the quality of learning to create education with more character. In design stage, reflective thinking strategies are designed to train students' reflective thinking skills in writing texts in the form of long, genre-based functional texts. In development stage, material feasibility tests, language use, and appearance are tested by validators through a validation process. The development of this worksheet began to develop through criticism and ideas on the validator sheet that had been provided. Further revisions and analysis are carried out to then produce the final writing worksheet. In disseminate stage, Module distribution was also carried out for vocational higher education in English to disseminate student writing worksheets to other vocational universities in the form of e-books and printed books.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".