Language Learning Strategies of Two Indonesian Young Learners in the USA
Bibliographic record
Abstract
This study attempts to discuss the English language used by Indonesian young learners who come to the US and identify their language learning strategies. Some theoretical issues in second language acquisition related to this topic are discussed and then followed by the discussion on the subjects’ learning strategies and some factors related to the strategies, and the description of the subjects’ language development. The two subjects are good English learners: they had “three variables” of good language learning: aptitude, motivation, and opportunity. They were bright children and they knew how to use their knowledge in learning a “new” language; they were good guessers and risk takers. They were also integratively motivated: they practiced their English; they were expressive and eager to communicate; and finally they had now a good opportunity to learn English in its natural setting. The main implication of this study is on the teaching of English to the Indonesian speakers. It is obvious that the differences between English and Indonesian sentence structures have created difficulties to the learners. A teaching syllabus based on contrastive analysis will be more realistic though only in obvious contrasting features. It should reinforce marked differences in L2, where L1 and Universal Grammar are unmarked – so as to raise learner’s awareness of the new features.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".