Investigating the English Language Needs of the Female Students at the Faculty of Computing and Information Technology at King Abdulaziz University in Saudi Arabia
Bibliographic record
Abstract
In the field of computer science, specific English language skills are needed to facilitate the students’ academic progress. Needs analysis is generally believed to be an important element in ESP/EAP context because it enables the practitioners and curriculum designers determine the learners’ needs in a particular academic context. In this regard, this paper, adopting a quantitative research design, reports on a survey conducted to investigate the English language needs of the female students studying in the Faculty of Computing and Information Technology (FCIT) at King Abdulaziz University (KAU). The study aims at identifying the students’ perceptions about the importance of the English language skills, the frequency of using those skills, their ability levels in performing such skills, and their preferences regarding the English language course. The participants in this study are 135 female undergraduates who are studying at the third, fourth, and fifth year at the FCIT, during the academic year 2013-2014. The study identifies the students’ English language necessities, lacks, wants, and their perceptions of the current English course. The paper concludes with several pedagogical implications which seek to improve the current course structure and contents so as to cater for the students’ academic English language needs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".