Bibliographic record
Abstract
This article reports on a case study that investigates the learning preferences and strategies of Chinese students learning English as a second language (ESL) in Canadian school settings. It focuses on the interaction between second language (L2) learning methods that the students have adopted from their previous learning experience in China and ESL instruction they have encountered in the North American classroom. Twenty Grade 9–10 Chinese immigrant students in a Canadian urban public high school participated in the study. Data derived from questionnaires and interviews show that the students pragmatically searched for strategies to optimize their English learning by taking advantage of their first language skills and available scaffolding. In particular, they considered the explicit strategies they had experienced in first language (L1) and English as a foreign language (EFL) instruction before their arrival in North America to be valuable. This article proposes a resource-oriented functional approach to teaching and learning ESL. To that end, it argues that language-teaching strategies often used in North American ESL classrooms can integrate more explicit teaching and learning strategies to help students, particularly those with beginner levels of English proficiency, to acquire vocabulary more effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".