Readability of Reading Texts for EFL Students at Al-Baha University
Bibliographic record
Abstract
Reading is an important skill for EFL university students, as it reinforces their critical and analytical thinking. EFL university students rely on reading texts to expand their knowledge of the outside world and enhance their memory functions, enabling them to use the target language more effectively. EFL instructors depend on the reading texts as the basis for curricular and extracurricular activities to enhance their learners' language skills, enabling them to use the language in everyday situations. Therefore, selecting suitable English textbooks with appropriate reading texts is crucial for students' achievement and progress. Research in the areas of readability of textbooks and reading texts is very important to assess the appropriateness of different texts for learners. Many readability studies have focused on examining the suitability of readability formulas for measuring the readability level of texts provided to learners. This study measured the readability level of the reading texts of Evolve 4 special edition (published by Cambridge University Press) offered to EFL university students using online digital software that utilizes a set of the most commonly used readability formulas, including the Flesch Reading Ease Formula, Gunning Fog index, and Coleman Liau index. This study also used a set of cloze tests administered to female EFL university students, designed by the researcher to measure the readability level of the reading texts and provide suggestions and recommendations for EFL instructors who teach the textbook at the university level. The findings of this study show that the readability level of the reading texts in the English book Evolve 4 special edition is fairly challenging for EFL university students, as measured by a set of readability instruments. Consequently, the study recommends that EFL instructors should utilize a diverse set of teaching strategies and cooperative work methods to help their students read and understand the reading texts in Evolve 4 special edition. Moreover, EFL instructors should take into account significant factors that can affect the readability of English reading texts, such as learners' interest, prior background, style of the text, and the content itself. These considerations should guide instructors in tailoring their approach to maximize student learning outcomes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".