Preparing Diverse Learners for University: A Strategy for Teaching <scp>EAP</scp> Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With a burgeoning international student population, most universities around the world offer English for Academic Purposes ( EAP ) courses. Because classes are so diverse, it is challenging to meet the specific needs of EAP students. Keeping this status quo as a departure point, the authors discuss a five‐prong strategy for teaching EAP , which involves academic culture acclimatization, student voice, teachable moments, reflection, and autonomy. They discuss this teaching strategy with specific examples, arguing that it helps provide a common reference point that all EAP instructors can use as heuristics, regardless of the context of their teaching. In addition, it promotes student‐centeredness in the EAP classroom and encourages students to become more involved in the learning process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it