Analysis of Learning Strategy on the Improvement of the Listening Comprehension Ability of Non-English Majors in Engineering Colleges from the Perspective of CSE: A Case Study of NCEPU
Bibliographic record
Abstract
In today’s world of increasingly frequent global cooperation and communication, English has become an indispensable communication tool. In the process of communication, listening comprehension skills are especially important to have an oral output. For a long time, the public English courses in engineering colleges and universities don’t have enough listening course settings for non-English majors, which cannot meet the needs of non-English learners since they also have to obtain cutting-edge scientific information, such as from relevant scientific lectures. In 2018, China’s Standards of English Language Ability (CSE) was officially released, which provides a comprehensive, clear and detailed description of the characteristics of each listening comprehension level(Guo Xiaoting, 2018). In December 2019, the scale was officially docked with TOEFL scores, highlighting the scale’s role as an ability assessment standard for English learners (Qiu Chenhui, 2019). In this paper, a questionnaire survey was conducted on the English listening comprehension ability of non-English majors of North China Electric Power University, using CSE as the assessment standard. It tries to draw out existing questions and proposes relevant strategies for practical research, and then test these strategies’ effectiveness through Eviews software.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".