Experiential Study in Learning English Writing: An Inquiry into Saudi Learners’ Concerns
Bibliographic record
Abstract
Writing plays a prominent role in learning a second language or foreign language. Research mainly focused on the development of writing skill from the teachers or trainers’ perspectives, but in the recent studies researchers have explored students’ concerns about writing skill and the difficulties they encounter in the process. The learners are fully aware of the challenges and their expectations of the courses. The purpose of this study is to investigate students’ perceptions about academic writing courses or writing in general and to investigate the differences they observe about the teaching practice followed in the kingdom as well as ELP in the USA. A total of 03 students (from three different provinces of the KSA) from the pre-university English language program (Applied English Center, Kansas University, USA) for Saudi students enrolled for various courses in their masters program in the US universities participated in this study. The general design of the study was qualitative in nature as a questionnaire and a focus-group interview were implemented for data collection. The overall results demonstrated that the difference in teaching strategies is a significant factor which poses a question mark on the professional training expected of the teachers involved in various universities in the kingdom. The major findings demonstrated students’ awareness of their needs and ESL writing requirements and how teaching writing in MT or so to say L1 influences their ESL learning. The study concluded with recommendations for future 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".