Bibliographic record
Abstract
The aim of the current study was to investigate the reasons behind Saudi learners’ poor reading skills. To this end, the objectives were to identify the reading habits of Saudi English as a Foreign Language (EFL) middle school learners, to understand the extent to which Saudi EFL middle school learners use reading comprehension skills and to explore the perceptions of learners, teachers and supervisors regarding Saudi EFL learners’ reading abilities. The study sample consisted of 90 Saudi EFL middle school students, eight EFL teachers and six supervisors. The students were surveyed and interviewed about their reading habits and use of reading skills, while the teachers and supervisors were interviewed to explore their perceptions about reading instruction in the Saudi context. The results revealed that most Saudi EFL students lack the necessary reading habits in L1 and L2. In addition, they rarely make use of important reading skills when they read English texts. This study identified “lack of exposure to target language”, “poor teaching skills and teacher training programs”, “little attention to comprehension and more attention to reading aloud”, “students’ lack of motivation”, “little emphasis on reading skills in textbooks”, “unfamiliar and unsuitable reading topics”, “lack of reading skills training for students”, “students’ limited vocabulary” and “lack of parental involvement” as the most important factors behind Saudi students’ poor reading abilities. The study concluded with important recommendations and suggestions for future research.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".