Transforming Passive Listeners into Active Speakers: A Study with Portuguese Undergraduates in ‘English for the Social Sciences’
Bibliographic record
Abstract
In the present context of English as a Lingua Franca (ELF) or English as International Language (EIL), it has become extremely relevant to maximize speaking opportunities in the English as a Foreign Language (EFL) classroom which aim at developing fluency and real-life communication skills. University students in Portugal need to practice expressing their views and sharing opinions in English for their professional careers and for general international communication. With these aims in mind, ‘whole-class discussions’ were implemented in the subject ‘English for the Social Sciences’, but monitoring by the teacher revealed low voluntary participation. This classroom-based study began with two questionnaires distributed to a group of twenty intermediate to upper-intermediate undergraduates. They exposed speaking anxiety due to worries about producing accurate English, fear of poor performance and negative evaluation, and embarrassment to speak in front of colleagues. A two-step strategy was implemented to get as many students as possible speaking, by overcoming some of their linguistic and personality barriers. First, students attended two awareness-raising lessons on the aims of whole-class activities, the value of fluency, and today’s real-life communicative skills. Then, small ‘buzz-group’ discussions were implemented, prior to and in preparation of the ‘whole-class discussions’ to create a more supportive environment. Student interviews and teacher observation notes were used to collect participants’ perspectives. Findings revealed that passive listeners, when better informed and working in a less anxiety-inducing environment, can become active speakers.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".