Non-English Majors’ Listening Teaching based on Lexical Chunks Theory and Schema Theory
Bibliographic record
Abstract
<p>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.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".