Are You Listening Strategically or Randomly? Examining Listening Comprehension Strategies Among Saudi University EFL Students
Bibliographic record
Abstract
Listening is obviously an essential skill for anyone who wishes to acquire a new language, but second-language (L2) listening research is scarce compared to research on L2 reading. Additionally, the literature indicates that Saudi students, regarding listening, lag behind students from other nationalities on standardized international language tests. Hence, in this study, I examined the use of L2 listening comprehension strategies in an English as a foreign language Saudi university context. Based on an existing 19-item psychometrically validated questionnaire followed by a listening test, the results revealed that students were aware of L2 listening strategies and used cognitive strategies slightly more often than metacognitive strategies. However, notably, their less frequent use of practice-based self-regulation listening strategies (e.g., having good partners in practicing listening skills) could adversely impact their L2 listening success. The results also showed a rather weak significant difference between the listening strategic trait and listening test performance; a weak negative significant relationship between listening strategies, age, and years of learning English; and an insignificant relationship between listening comprehension strategies and academic level. However, the results suggest that the number of years of learning English is linked to better listening test performance. Finally, the items in the existing questionnaire, which was employed to assess listening comprehension strategies, lacked internal consistency and had problematic subscales; nevertheless, an acceptable one-factor model emerged from this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".