English Language Speaking Skill Issues in an EMP Context: Causes and Solutions
Bibliographic record
Abstract
A good start in researching on language teaching and learning issues is to first analyse target learners’ actual performance and their needs. This mixed-methods 2-cycle study is aimed to analyse medical-college students’ language needs through two instruments—a self-rated report and a guided focus group. Out of the main four language skills (speaking, reading, listening, and writing), Cycle 1 aimed at exploring the most trouble-provoking skill for EMP students through a 7-item rating report with a sample of 45 participants. Based on the results of Cycle 1 which labelled speaking as the most problematic language skill for the target learners, Cycle 2 proceeded with 9 interviewees to narrow the study focus on the factors contributing to the inefficiency of speaking skills among EMP learners, discussing solutions from the learners’ perspectives. Pedagogically, this research helps practitioners innovate and integrate new techniques in language teaching and learning to overcome the issue of students’ speaking performance that has been deemed below expectations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".