Bibliographic record
Abstract
As a means of communication, listening plays an important role in people’s life. In foreign language classroom, listening comprehension has never drawn the same attention of educators as it now does. So it is a vital importance to teach aural English more effectively. In view of present situation of aural English teaching and wrong ideas about it, the problems in traditional aural English teaching have been discussed, including monotonous pattern of teaching, ineffectiveness of teachers’ roles, students’ passivity, orientation at exams instead of students’ abilities and so forth. Then suggestions are presented on how to teach aural English more effectively: first, diversifying patterns of teaching should throw the emphasis on teaching in authentic environments and interaction between listening and other teaching activities; secondly, teachers should design listening activities for the class, build good interaction in the class and cultivate more creative methods in their teaching to change their ineffective roles; thirdly, students’ passive roles in class should also be modified by harmonizing their extrinsic motivations and intrinsic motivations; finally, the relationship between exams and development of abilities should be coordinated by using different strategies in different cases. Yet, there still exist a lot of problems in aural English teaching. For example, how to use authentic recordings in aural English teaching? Is it necessary to have audio equipment in order to train listening skills? And how to build the listeners’ confidence in listeners? etc. Therefore, there is still a long way to go for EFL educators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".