Experiences of Intensive English Learners: Motivations, Imagined Communities, and Identities
Bibliographic record
Abstract
Based on a widely held belief that immersion provides the best language learning opportunities, a large number of Asian students go to English-speaking countries to improve their English language skills. These strongly motivated learners arrive in a new country with a bag of expectations, learner beliefs, and imaginations about the new community they are about to enter. However, learners are often faced with a set of challenges with respect to language learning opportunities and identity negotiations in the new community. Against this background, the present study examined three cases of Korean learners enrolled in an intensive English program (IEP) in the U.S. The aim of the study was to understand distinct struggles experienced by this learner population. The study found that the participants had certain motivations and imaginations about the new communities, but they experienced numerous challenges and struggles particularly with regard to opportunities for authentic communications in English and identity conflicts. The paper discusses pedagogical implications for an effective IEP curriculum to allow students more opportunities for legitimate periphery participation in the target language communities of practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".