Bibliographic record
Abstract
Of all English Language skills, writing poses the greatest challenge for students due to the demands of style, structure and vocabulary. Even if second language learners (L2 learners) can speak the language well enough for everyday activities - shopping, traveling, and so on, producing an academic write-up that is precise, accurate, objective and fully referenced is still quite a task. This study aimed to determine the academic writing strategies used in ESP classrooms. Along with this, the study determined the perceived proficiency of L2 learners in academic writing, based on their ESP test course. The study also reports the needs of L2 learners in academic writing, and how English for Academic Purposes (EAP) instructors can help to improve the writing skills of L2 learners. The study participants consisted of 60 L2 learners from various departments in King Saud University. A questionnaire was used to gather the responses of participants. The data was analyzed using SPSS 20.0 software. The results are displayed in descriptive statistics - frequencies and percentages. Inferences were made from the quantitative data, which formed the bases of discussion of the results of the study. The study found that L2 learners consider their academic writing skills to be adequate. This was reported as perceived proficiency since previous studies have reported discrepancies between the perception of teachers and students. L2 learners also revealed that they need to improve on grammar, vocabulary and punctuation as well as the use of academic writing strategies. The study revealed that majority of the respondents use strategies such as outlining and brainstorming. L2 learners performed above average when they use these writing strategies. However, L2 learners want EAP instructors to improve on core ESP topics including grammar, vocabulary and the use of writing strategies. Still, others want EAP instructors to improve on their teaching methods, as well as create an all-inclusive environment for students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".