A Review on Language Learner Autonomy Research in China (2006-2016): Based on 12 Key Domestic Journals
Bibliographic record
Abstract
Since the implementation of the 2001 curriculum reform and the release of the 2004 college English requirements, learner autonomy has become one of the most important research themes in the field of ELT in China, especially college English, reflecting the tendency of a learner-centered teaching approach. In this paper, I have reviewed 39 studies about language learner autonomy from 133 articles selected in key journals targeting language education published in mainland China from 2006 to 2016, so as to see what have been the features of related research through the years. In the review process, it is found that influencing factors, improving methods as well as evaluation approaches of learner autonomy have been the dominant themes that attract Chinese researchers’ concern. Among them, improving methods of language learner autonomy have drawn even more attention from Chinese scholars, which have mainly addressed issues related to teachers, learner training and autonomous learning environment. It is also observed that empirical studies have been dominant through the decade and more types of students have been involved in research. However, I also note that there is still deficiency with such studies of language learner autonomy, so suggestions are provided for researchers in the field.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".