Reading Comprehension Difficulties Among EFL Learners in Higher Learning Institutions
Bibliographic record
Abstract
One of the most significant problems faced by instructors is reading deficiency in English texts among the university students, which reflects the students’ poor academic performance. It is assumed that learners who are unable to read and comprehend face many challenges during their studies and after graduation. This study aims to investigate reading comprehensions difficulties among EFL learners in higher learning institutions. The study employed quantitative method, 100 out of 281 Arab students of Universiti Sultan Zainal Abidin (UniSZA) and Universiti Malaysia Terengganu (UMT) were selected to participate in responding to the questions. Cross tabulation was used to analyze data from the test. Findings from the test indicated that the major difficulty faced by the Arab EFL learners is inability to recognize the types of text. This study concludes that the reading comprehension difficulties faced by Arab EFL learners in the selected institutions could affect their English language proficiency and academic performance. To find solutions to these difficulties, there is a need for shared efforts of English language teachers, instruction policy makers, public and private bodies responsible for educational policy learning and implementation, and the EFL learners.
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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 | 0.005 |
| 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 | 0.001 |
| 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 | 0.000 |
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".