L2 Listening Comprehension: Is it a Language Problem or Listening Problem?
Bibliographic record
Abstract
In a foreign language environment, students typically have limited exposure to the language outside formal classrooms. Therefore, their ability to comprehend spoken English may be limited. To add to this problem, L2 learners often regard listening as the most difficult language skill to learn. On the other hand, it is noticeable that L2 listening remains the least researched of all four language skills. Accordingly, the present study is based on the commonly believed premises that (1) investigating the listening comprehension process can provide useful insights into teaching listening and (2) learners who learn to control their listening process can enhance their comprehension, and their overall proficiency may be highly developed.The present study reports on the results of an empirical study on forty-six L2 learners of English. The subjects were equally divided into two groups. The first group (N=23) represents first year students (Beginners) in the Department of English at the Faculty of Education, Menufia University, Egypt. The second group (N=23) represents fourth year students (Advanced) in the same department. The major question that this study attempts to answer is “whether listening comprehension a language problem or listening problem?” The instruments of this study consist of five tasks: pre-test, questionnaire, classroom instruction sessions, post-test, and interviews. The data analysis had a quantitative and a qualitative part. Results were obtained and conclusions were made.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".