English for General Academic Purposes:Catering for Discipline Specific Needs.
Bibliographic record
Abstract
The number of international students who come from non-English speaking backgrounds to countries where English is the first language is on the rise, necessitating the provision of English for Academic Purpose (EAP) courses to prepare such students to study in English-speaking institutions. Because of the variety of academic backgrounds of students entering these courses, classes take the form of English for General Academic Purposes (EGAP), which mostly comprise study-skills based approaches. It is often difficult to relate such EGAP content to students’ academic disciplines or to address their academic needs in classes which contain students from a range of academic backgrounds. In order to address this problem, disciplinespecific tasks were integrated into an EGAP context and the effects evaluated. The following indicators were used to check how successfully the approach addressed the academic and linguistic needs of students: student needs analysis; feedback from questionnaires and student interviews; participant observation; and end-of-course evaluation. The findings indicated that the students had a strong preference for discipline-specific tasks and the discipline-specific approach was successful in relating the EGAP content to students’ academic disciplines and in addressing their language difficulties. Towards the close of the 20 th century there were more than a million students in higher education worldwide who were studying outside their own countries (Huxur, Mansfield, Nnazor, Schuetze, & Segawa, 1996). The majority of these students were from non English speaking backgrounds (NESB) and they studied in English speaking countries such as the UK, USA, Ireland, Canada, New Zealand and Australia. These numbers increase every year. Consequently, in recent years,
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".