Do Language Proficiency Levels Correspond to Language Learning Strategy Adoption?
Bibliographic record
Abstract
The primary focus of research on employment of language learning strategies has been on identification of adoption of different learning strategies. However, the relationship between language learning strategies and proficiency levels was ignored in previous research. The present study was undertaken to find out whether there are any relationship between the employment of different strategies and learners' levels of language proficiency. To this end, initially, a simulated TOEFL test (Bailey, R. F., Seetharaman, S., Gavin, C. A., Shukla, N., Penfield, J., and Subramanian, R., 1993) was administered to classify the learners into three classes of proficiency levels: beginning, intermediate, and advanced. Then, Oxford's Strategy Inventory, SILL, (Oxford, 1990b) was used to determine the frequency of the language learning strategies applied by learners. The results indicated that there is a direct relationship between employment of different strategies and proficiency levels. Therefore, the findings, in general, seem convincing enough to enable one to claim that there is a correspondence between the employment of different strategies and proficiency levels. The results of the present study are by no means complete. More research is needed to substantiate the outcome of the current study. One pedagogical implication of the study is that language instructors and syllabus designers should be advised to inform language learners about language learning strategies. Other implications have been discussed.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".