College Instructors’ Focus in the EFL Writing Classrooms: An Exploratory Study
Bibliographic record
Abstract
EFL is one of the two most liberally funded academic streams in Saudi Arabia after science and technology. Universities across the country are replete with faculty that boast of enviable national and global exposure. Even so, learner standards, especially in writing skills, are way below those expected which highlights the need for rigorous research into what it is that drives the EFL teachers’ choices in the classrooms. This study carried out a systematic evaluation of teachers’ focus in the writing classes at Qassim University, with four parameters isolated for data collection: (i) writing approaches; (ii) writing performance; (iii) writing strategies, and (iv) writing skills. The study tool is a survey with 46 teachers engaged from undergraduate to research studies at the University. The findings showed that writing approaches were the highest focused variable among the EFL faculty followed by writing performance, writing strategies and lastly, writing skills. The findings showed the tendency of EFL teachers to focus on areas that do not directly help in developing their learners' ability in writing and hence, EFL teachers/ instructors’ need to focus on the practical side of the writing skills and providing adequate exposure to their learners in group, pair and individual tasks to master the writing skills. Finally, assessment needs to focus on measuring the practical side of the learners’ knowledge rather than blindly testing them on their ability to reproduce their learning.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".