Analysis of Perceived Difficulty Rank of English Skills of College Students in China
Bibliographic record
Abstract
A widely known fact of English education in China is that after years of learning, most Chinese students still can not speak English or understand spoken English. This phenomenon is called “mute English” in China’s academic community, a phenomenon that has been frustrating both English instructors and learners ever since English was listed a compulsory subject in high schools and mandatory course in universities in China years ago. “Mute English” or “English mute” is due to many factors, which would require more than one study and constant research efforts to arrive at a full understanding. This study analyzed the perceived difficulty rank of five English skills, namely, listening, speaking, reading, writing and translating in the hope of discovering the relationships among these skills to find out how these skills interact with each other, thus providing some hints on improving China’s current English teaching approaches and reducing the mute English phenomena. Data analysis shows that of listening, speaking, reading, writing, and translating, Chinese students perceive translating and speaking the most difficult skills to command, whereas reading is perceived the least difficult one. However, no significant difference is found between translating and speaking between English majors and none majors. Such perception differences in these English skills alert us that we need to restructure our design teaching approaches and course contents for an effective teaching approach of oral English in China and to reach the ultimate goal of the application of foreign language learning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".