A Study of Affective Factors Affecting College Students' Autonomous English Learning
Bibliographic record
Abstract
Chinese colleges have traditionally placed a high value on the development of students' "autonomous learning" abilities, with the goal of helping them learn English on their own being one of the main teaching goals. The way that students learn English autonomously is influenced by a variety of elements, including emotional factors, classroom teaching styles, learning methods, and more. This study aims to investigate how students' emotional factors—learning motivation, anxiety, attitude, and teacher-student relationship—affect their autonomous learning of English. In this study, we conducted a questionnaire survey among 193 non-English majors in Kunming and conducted interviews with some students. Research indicates that learning motivation, anxiety, attitude, and other emotional factors significantly influence students' autonomous learning. Learning motivation and autonomous learning are positively correlated; anxiety negatively affects students' learning and self-confidence; learning attitude and autonomous learning are closely related; and developing a positive learning attitude is essential to learning a foreign language. Therefore, we should utilize all available educational resources to increase students' intrinsic motivation for learning; implement efficient learning techniques to lessen the incidence of learning anxiety; make clear the purpose of autonomous learning; and adjust students' positive learning attitudes to boost the effectiveness of autonomous learning in English.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".