Bibliographic record
Abstract
Learning context, as an important influence on language learning strategy (LLS), has received more attention in recent years. For the purpose of better strategy choices in different learning contexts and language achievements, this study investigated LLS from the perspective of learning context. Participants were 44 doctoral students recruited in ESL learning context (the US) and EFL learning context (China). Data were gathered through a mixed-methods research design with the use of the Language Strategy Use Inventory (LSUI). The results showed that the participants in both learning contexts reported to use similar LLSs in terms of listening, speaking, reading and writing. However, some strategies were used significantly more often in one learning context than the other (e.g., Imitate the way native speakers talk was used more often in ESL context; Switch back to my own language momentarily if I know that the person I’m talking to can understand what is being said is used more often in EFL context). The results also showed that a diversity of additional LLSs were differently used in each learning context. The findings revealed that learning contexts have influence on strategy choice. Furthermore, insights for future LLS research in contexts were offered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".