Exploring Learner Autonomy for Language Proficiency Development: A Mixed-Methods Study in Bangladeshi EFL Education
Bibliographic record
Abstract
This study investigates how learner autonomy helps Bangladeshi students improve their English.Learner autonomy means that a student can take charge of his or her own learning.Many experts say this is important for learning a second language.But in Bangladesh, this idea has not been studied much.The study used both numbers and interviews.First, a survey and a test were done with 120 university students.The results in this regard showed a strong link between autonomy and English level.The correlation was r = .61,p < .01.The analysis showed that autonomy explained 37% of the difference in test scores.Second, interviews were done with 12 students and six teachers.Four main points came out: i) students want more responsibility and control, ii) exams make it hard to practice autonomy, iii) teachers play an important role and iv) technology can help students learn by themselves.Both learners and educators mentioned that autonomy is beneficial.But they also said there are big problems.Class sizes are too big and they are too exam-focused.Besides, the control of the teachers over class is very high.Technology helps.But students need guidance to use it fairly well.The findings show that autonomy is both a predictor of English skill and also part of daily life in Bangladesh classrooms.Culture and system factors affect it.The study concludes that autonomy can grow if the system changes and there is a need for teacher training, learner support and smart use of technology to facilitate the changes.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".