Is Critical Reading Indispensible to College English for General Purpose in China
Bibliographic record
Abstract
This paper aims to depict the current situation of application of critical reading skills and strategies among the undergraduates in reading books in English in a local university in the coastal city in China. The findings turn out that critical thinking strategies are used neither automatically nor frequently among the undergraduates. The findings are complemented by data collections from classroom observations, interviews, questionnaire, reading comprehension tests and notes taken in class. In order to have a better understanding of the reasons underlying the infrequent use of critical reading skills and strategies, a qualitative study was conducted in 12 volunteers. This study also reveals that college English for general purpose has, to some extent, contributed to improvement of students’ use of critical thinking skills in reading. It is worth pointing out that the role of College English for General Purpose is not merely for imparting use of English to students but also taking on a vital role of cultivating students’ critical thinking and enhancing the application of critical thinking skills and strategies in reading. This paper has implications for college English teachers in their teaching practice for desirability of enhancing critical thinking in college students. Explicit and systematic teaching of critical thinking strategies is surely encouraged, complete with critical writing in the process of teaching College English for the General Purpose.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".