A Process Genre Approach to Teaching Report Writing to Arab EFL Computer Science Students
Bibliographic record
Abstract
In the teaching and learning of EFL writing, the Process Genre Approach (PGA), an integration of the process approach and the genre approach, has recently received much attention worldwide. This approach, however, has not been given enough focus in the Arab EFL context. The purpose of this paper is twofold: to report an implementation of a process genre approach in teaching a report writing course; and to explore views of the Arab EFL students attending that course. The study employs two instruments for data collection: observation, for describing the implementation of the PGA; and a questionnaire specifically designed for eliciting students’ views. Participants are 17 students who attended a report writing course in a computer science department at a university in Yemen. A description of the implementation of the approach is presented in five main areas: preparation of form; preparation of genre; planning, drafting and revising; feedback; and teacher roles and scaffolding. The findings revealed positive views of computer science EFL students on using the process genre approach in teaching report writing. The study concluded with relevant implications and recommendations for Arab EFL writing teaching and research.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".