A Survey of Students’ Attitudes Toward English Graded Teaching in China: A Case Study of North China Electric Power University
Bibliographic record
Abstract
The present study investigates students’ attitudes toward English graded teaching in North China Electric Power University (NCEPU for short). In essence, differences between learners of different grades and achievement groups were examined with regard to five constructs, namely, their perception of English graded teaching, the impact of English graded teaching on their learning, their attitudes toward teaching materials, their attitudes toward teaching content and their expectations for college English instructions. The study also aims to put forth proposals to alter the undesirable aspects of college English graded teaching. Sixty-six students from ten departments participated in the survey. Questionnaire containing both questions with best choices and open-ended questions was the main instrument. Analysis of the quantitative and qualitative data shows that students from different achievement groups tend to hold neutral attitude toward our teaching practice, the teaching materials, and the teaching content. Despite this tendency, students from high achievement group tend to take positive attitude toward the undesirable aspects of this teaching practice. Finding from the questions with best choices indicates that our teaching practice may have lost its appeal to the language learners due to its failure to stimulate their motivation and meet their demand and interest. In addition, results of open-ended questions reveal that alterations are needed in teaching contents, teaching focus and assessment system if it were to regain students’ interest. The results of the study contribute to a good understanding of current English teaching situation in NCEPU. Proposals to alter this undesirable situation are put forward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".