Academic English is No One’s Mother Tongue: Graduate and Undergraduate Students’ Academic English Language-learning Needs from Students’ and Instructors’ Perspectives
Bibliographic record
Abstract
A research project designed to assess English-as-first-language (EL1) and English-as-an-additional-language (EAL) undergraduate and graduate students’ academic language-learning needs in the context of an academic language-support unit was conducted. This paper reports findings pertaining to 370 EL1 students and 88 instructors at the graduate and undergraduate levels. These participants responded to questionnaires, which requested them to rate the importance of academic language skills, to assess their own or their students’ skill status, and to respond to open-ended questions regarding their own or their students’ academic communication challenges. In addition to reporting EL1 students’ perceived needs and assessments of their skills, a comparison of findings between EL1 and EAL contexts is presented. Findings point to a match between instructors and students at both the graduate and undergraduate levels in their perceptions of important academic language skills, but a great divergence in their assessments of students’ competence in those skills. These findings indicate a need to re-examine the divide often made in English for Academic Purposes (EAP) programmes regarding divergent needs of EAL versus EL1 learners as well as to determine whether the convergence of their needs can be considered when planning EAP courses or workshops, especially during challenging economic times, when priorities must be set in response to the rise of international EAL student enrolment in English-speaking countries.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".