The Influence of Curriculum of English Major on Oral Communicative Competence: A Case Study of Zhejiang University of Science and Technology
Bibliographic record
Abstract
With China’s integration into the global economy, there is a growing demand of college English major students qualified for oral communicative proficiency. Speaking is the most direct and efficient way of communication. Therefore, oral communicative competence is the top priority for cultivating English major students. However, at present, most of the English major students are facing a hard dilemma that their overall English oral communicative ability is weak, their output ability is incompetent and their accuracy is not acceptable. Moreover, the oral English teaching in large-scale classes for English major is far from satisfactory. Utilizing curriculum settings in an effective way to help English major students increase the fluency of the language output. Oral English classroom plays an important role in guiding students. The survey found that the majority of students believe that current oral curriculum settings has some problems and does not work well. What pedagogical goals should be achieved and what kinds of lessons should be presented in the English major oral classrooms also provoked our thinking. To alleviate students’ oral anxiety, refining the curriculum with an emphasis on oral instruction, clarifying the goals of such teaching, and enhancing assessment techniques might be effective measures to improve students’ oral competence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".