Exploring Listening Strategies Employed by EFL Learners in Question and Response Tasks
Bibliographic record
Abstract
This study aimed to explore listening strategies EFL learners use when completing mock TOEIC listening tests; investigate listening strategies that lead to success in answering the test questions; and find out how differently high-, intermediate-, and low-proficiency learners use listening strategies in four different task types. A total of 23 participants were selected for stimulated recall protocol interviews. Verbal reports from each participant were coded using taxonomy, and each strategy participants used was grouped according to listening task type. The results from the stimulated recall protocol interviews revealed that participants employed identification of words and chunks, hypothesis formation, monitoring against the question, and matching lexis heard to lexis in the question strategies to help them arrive at the answers in the question and response part. Learners with the three levels of proficiency employed similar strategies in their listening test. However, the frequency and quality of the strategies used to help them arrive at their answers were completely different. Learners whose linguistic knowledge was limited struggled to apply listening strategies to solve listening problems, whereas learners whose linguistic knowledge was automatic were able to comprehend the listening passage and apply appropriate strategies synchronously to solve listening problems.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".