28. How Much Language Do They Need? The Dilemma English-Medium Universities Face When Enrolling English as an Additional Language Students
Bibliographic record
Abstract
Although international and domestic students applying to English-medium universities may well meet the minimum language entry requirement, recent research indicates that this level of language proficiency often does not provide students with the means to cope effectively with their academic studies (Barthel, 2007; Elder, 2003; Read & Hayes, 2003). To resolve this dilemma our major, multicultural New Zealand university is addressing the problem through implementation of the Diagnostic English Language Needs Assessment (DELNA), a post-entry programme administered to all first-year undergraduate students, regardless of their language background. We use the diagnostic outcomes to guide individual students with particular needs to appropriate forms of academic language enrichment. This paper outlines DELNA’s history and administration, student responses to the assessment and the subsequent development and uptake of language support options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".