Same Size Doesn't Fit All: Insights from research on listening skills at the University of the South Pacific (USP)
Bibliographic record
Abstract
Listening skills research has tended to focus on strategy use in classrooms and on theory and practice of second language (L2) teachers. This study examined the teachers’ and learners’ perceptions of listening skills in non-classroom learning situations. Five (n = 5) study skills teachers and 19 former learners in a distance study skills course at the University of the South Pacific (USP) were interviewed for this study. The interviews with the study skills teachers sought their expectations of their learners’ listening strategies, their views about the learners they taught, and the skills their learners used for listening. Former learners were similarly questioned about their perceptions of listening strategies they were taught and used. Data was collected and managed using NVivo, a computer assisted qualitative data analysis software. Besides revealing strategies that distance learners reported using their learning listening skills, the study identified a number of differences in views presented by researchers and L2 teachers, as well as differences in perceptions on listening skills between L2 teachers and L2 learners. The paper concludes that there exists a discrepancy between research and the practice of researchers, L2 teachers, and L2 learners on what works. The author also recommends further research in this area is needed, because research examining classroom-based learning situations will likely not apply to, nor fully inform, distance learning contexts.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".