Investigating Listening Comprehension Challenges in Online EFL Courses: Perspectives from University Students
Bibliographic record
Abstract
The current study investigated the challenges of listening comprehension encountered by Saudi university students in online English as a Foreign Language (EFL) classes. It examined the role played by gender and academic specialisations in the listening comprehension challenges in online EFL classes. Data were collected via an online questionnaire from five hundred and thirty-nine male and female undergraduates. The results indicated that the primary challenge was related to Listening Conditions, which ranked highest with a mean score of 3.78. This was followed by Language Exposure (mean score of 3.60), Suitability to Student Proficiency Level (mean score of 3.47), Well-being and Alertness (mean score of 3.41), and finally, Listening Skills (mean score of 3.40). Additionally, the results revealed statistically significant differences (p < 0.05) in the challenges of listening comprehension based on gender, with female students reporting greater difficulties than male students. Furthermore, significant differences (p < 0.05) were found concerning the students' specialisation, with those in health-related fields experiencing more pronounced challenges compared to their counterparts in scientific disciplines. This study emphasises the necessity of implementing strategies to enhance students' EFL listening comprehension and calls for more urgent and comprehensive research to address the identified challenges in listening comprehension.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".