How Successful Learners Employ Learning Strategies in an EFL Setting in the Indonesian Context
Bibliographic record
Abstract
<p>Numerous studies have been conducted to correlate the use of language learning strategies and language performance and the studies have contributed to different perspectives of teaching and learning a foreign language. Some studies have also revealed that the students learning a foreign language in Asian contexts have been proved to use different learning strategies compared to students that learn the same language in Western countries. The present study was conducted with 73 students of the seventh semester of an English Department in Indonesia. The subjects had been employing learning strategies in an EFL setting in the Indonesian context. The data in this study were collected by administering a questionnaire of language learning strategies and a test of language proficiency. With the Cronbach’s alpha of the measurement at .92 and the alphas of the skill–based strategies at .78, .75, .79 and .77 for strategies for listening, speaking, reading and writing respectively, the criteria on reliability of internal consistency for the 80 items of the measurement were very high so that the strategies were justified to be grouped into a scale. The empirical data in this study shows that different language skills were significantly correlated with the use of different learning strategies. The skill-based categories of language learning strategies introduced in this study, which cover cognitive, metacognitive and social strategies in each category, can be used to portray learners’ use of language learning strategies when learners learn the four language skills of English as a foreign language in Indonesian context. The findings suggest that language teachers need to condition the process of teaching and learning in order for their students to use effective learning strategies according to the language skills that leaners are learning.</p>
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".