Reading Practices of EFL Students: A Survey of Kuwaiti College Students
Bibliographic record
Abstract
Reading is important for students by contributing significantly to success in their studies and their career development. A questionnaire-based survey was conducted in the English Department of the College of Basic Education, Public Authority for Applied Education and Training (PAAET), in Kuwait. Based on 410 responses of EFL college students in Kuwait on their reading practices, it was found that students read mainly for entertainment, and reading does not appear to be a popular activity among students. Fiction, fashion, and best sellers were the three main types of reading, indicating that academic reading was not a priority. Only a small proportion of students used e-books. Most students perceived such features of e-books as their portability and ability to store more information as likely to attract more students to e-reading, and indicated that they would be attracted to reading e-books themselves if circumstances change. This indicates a good potential to promote e-reading among students if steps are taken to make e-books and e-readers available to them through libraries and academic institutions. E-reading is also expected to become more popular among students if it could be linked to academic reading, particularly to the availability of text books in e-format. Libraries in Kuwait should start more proactive program to promote e-books and e-reading among college and university students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".