Graduate Students’ Proficiency Strategy Attitutes on Autonomous Learning in Foreign Language Learning
Bibliographic record
Abstract
The aim of the study is to investigate the strategy attitudes of graduate students’ proficiency attitudes on autonomous learning in foreign language learning. This survey was conducted in Balıkesir University in academic years of of 2014-2015. The survey analyses graduate (Master of Arts) students’ foreign language learning styles and strategies to find out to what extent they are autonomous. Two kinds of questionnaires were administered. The first one was learner autonomy survey questionnaire developed by Zhang and Li (2004), which was administered to investigate how autonomous the participants were in learning English as a foreign language. The second one was the perceptual learning style preference questionnaire (PLSPQ) developed by Reid (1987). The two questionnaires were administered to 600 graduate students enrolled in the Institutions of Social Sciences and the Institution of Health at Balıkesir University in the academic years of 2014-2015. Only 504 graduate students responded the questionnaires. Then it was announced that there would be two types of English YDS preparation courses for the participants enrolled at Balıkesir University, Institute of M.A programs. 30 participants applied to join the courses. The participants are assigned to two groups, as instructed and non-instructed on voluntary bases. The results of data analysis showed that most of the participants preferred to learn English in class. But few students believed that they would be successful by self-study. The learners did not use strategies in high level but they used them in medium level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".