Non-English Majors’ Listening Teaching based on Lexical Chunks Theory and Schema Theory
Bibliographic record
Abstract
English listening is seen as a vital means of linguistic input for Chinese EFL (English as a Foreign Language) learners, which lays a solid foundation for English learning and communication with English speakers. Besides, with increasing of scores of the listening part in the newly-reformed CET-4 and CET-6 (CET refers to college English test in China and both tests are the evaluation criteria of non-English majors’ English proficiency), it is urgent to improve non-English majors’ listening abilities in language teaching. However, students find listening to English stressful and painful and it is hard for them to process information quickly enough when listening. Meanwhile, their listening abilities cannot be improved effectively by the traditional English listening teaching methods. Researchers at home and abroad have discussed listening strategies, but seldom study the combination of lexical chunks theory and schema theory in improving non-English majors’ listening. Therefore, this research first proposes a lexical chunks schema-oriented listening teaching method which can effectively improve non-English majors’ listening abilities and then conducts an empirical study to verify its effectiveness. As the lexical chunks schema-oriented listening teaching method suggests, activities about memorization, recognition and reconstruction of lexical chunks, activation of the existed schema and building up new schema should be carried out in pre-listening, while-listening and post-listening in the listening class.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".