Revisit English Learner Autonomy Among Chinese Non-English Major Students During the COVID-19 Lockdown
Bibliographic record
Abstract
As the primary and immediate learning context, schools are underrepresented in learner autonomy studies. Scholars’ concerns over the intricate nature of schools lead to inadequate attention on the medium of learner autonomy development. To fill this research gap, a convergent mixed method design included a self-developed questionnaire, and four semi-structured interviews were employed to examine the non-English major sophomores’ learner autonomy during the COVID-19 pandemic. The triangulation of quantitative and qualitative evidence yielded that between a public and a private university, there was a statistically significant difference in English as a foreign language (EFL)s’ motivation for autonomous learning during the COVID-19 lockdown. But other than the degree of motivation, no difference was captured regarding EFLs’ belief and knowledge of autonomous English learning, as well as their metacognitive knowledge. Overall, EFLs were confident about their capacity to do autonomous English learning but engaged in a few systematical autonomous English learning during the COVID-19 pandemic. Based on the results, discussions over Chinese EFLs’ learner autonomy and possible explanations for the motivation differences are included. Pedagogy implications and limitations are elaborated on at the end. Plain Language Summary Using a mixed method design, this study reveals the English as a foreign language (EFL) learner autonomy and the role of schools in differentiating it, during the COVID-19 pandemic. The triangulation of quantitative and qualitative data yielded that despite mainfesting confidence in their capacity for autonomous learning, Chinese EFL learners did not engage in active, systematic autonomous language learning during the lockdowns. The motivation for autonomous English learning differed between universities, with learners from the less prestigious private University Qiu demonstrating more motivation than EFLs from the top public University Nan. The study contributes to understanding EFL learner autonomy during a challenging time of school lockdowns and their motivation issues. It highlights the discrepancy between learners’ self-reported autonomy and engagement in independent language learning. Additionally, it challenges the assumption that learners from prestigious universities would exhibit higher motivation for autonomous learning, showing that motivation can vary depending on the university context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".