International EAL Students’ Linguistic Self-Perception and Willingness to Communicate
Bibliographic record
Abstract
This mixed-methods study used the Willingness to Communicate framework to investigate international EAL students’ classroom participation in relation to both actual and perceived oral proficiency. We surveyed 41 EAL linguistics students from a Canadian university about their behaviors and self-perceptions on the dimensions of accentedness, fluency, and intelligibility. The speech of a subset of 19 students was recorded in an oral task and assessed by native-speaking listeners. The students also participated in semi-structured interviews about their linguistic experiences and self-perceptions. Quantitative results showed that EAL students felt that their language skills hold them back from participating in class. Participants were moderately accurate their L2 self-assessment, in contrast with previous research. Self-perception ultimately did not correlate with participants’ reported in-class behavior, suggesting that other factors influenced their decision to participate. In the qualitative results, students’ views of their speech were influenced by their knowledge of SLA and sociolinguistics. Students held conflicting attitudes, simultaneously recognizing that accentedness does not necessarily impede communication, while also expressing a desire to sound more native-like. Our results may assist post-secondary institutions in better supporting EAL students’ integration in the classroom.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".