An Investigative Study on the Listening Comprehension Strategies Employed in International English Language Testing System (IELTS)
Bibliographic record
Abstract
This study aims to explore the current situation of using Listening Comprehension Strategies (LCS) in IELTS, compare the difference in using LCS between the efficient listeners and less efficient listeners, find out efficient learners' specific methods of applying LCS, and provide some feasible suggestions for developing these strategies.This study collected IELTS listening scores of 166 sophomores from Sino-Canadian International College (SCIC) in November, 2015 and then investigated their uses of LCS through questionnaire.The result revealed that: First, students were able to use five LCS to a certain degree, and memory strategy was the most popularly used while meta-cognitive strategy was the least popularly used.Second, there was a significant positive relationship between LCS and IELTS listening achievements.Third, a significant difference was found between efficient listeners and less efficient listeners in using LCS.Fourth, no significant difference between male and female students appeared in LCS usage.Fifth, top students especially emphasized inferring, association, clause comprehension, sound recognition, and self-monitoring.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".