The Role of Recitation in the Process of English Learning for College Students of Science and Engineering
Bibliographic record
Abstract
Recitation--the traditional teaching method should be taken seriously again in English teaching. This is a survey report on the role of recitation in the English learning of College Students of Science and Engineering. The research questions are: (1) Do you think recitation is useful to improve English? (2) What recitation materials do you prefer to focus on, words, sentences, or articles? (3) Have you been required to do the job of recitation? (4) What is the source of their recitation materials? (5) What is the result of the last final English exam? According to the results of the survey, we come to the following conclusions: (1) College students of Science and Engineering believe that recitation plays a positive role in improving their English level. (2) College students of Science and Engineering think that recitation should be based on the textbook while adding some extra-curricular materials to expand the scope of knowledge. (3) Recitation is a matter within students’ duties, but teachers should regularly check the students’ recitation, which will promote their 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 | 0.010 | 0.046 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".