A Case Study on the Effectiveness of Learner Autonomy in British and American Literature Study
Bibliographic record
Abstract
It is generally acknowledged that learners should play an active role for themselves and take more responsibilities in studying a foreign language in order to improve learning efficiency. However, learner autonomy has not been paid much attention to in the present British and American literature teaching in Chinese universities. This situation has prevented British and American Literature teaching from playing its important role of cultivating students’ independent and creative thinking ability. Therefore, the author conducted an experiment in the classes of British and American literature in Foreign Language School of Inner Mongolian university for Nationalities, aiming at exploring the feasibility and effectiveness of cultivating learner autonomy in this course. In the research, a pretest (test before the experiment) and a post test (test after the experiment) were used as a comparision to collect data, and SPSS (Statistical Package for the Social Science) were used to analyze the results after the experiment. The analysis of the results and data shows that cultivating learner autonomy in British and American literature teaching can stimulate the students’ interest in this course and accordingly improve their strategies of learning this course. Besides, it can also improve their comprehensive ability of English study.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".