An Experimental Study of the Effects of Listening on Speaking for College Students
Bibliographic record
Abstract
As China enters WTO, more college graduates with higher oral English proficiency are required. However, we learned that even students in some distinguished universities are lack of this ability. Based on her teaching experiences and the theory proposed by Krashen and some other well-know foreign languages teaching researchers, the author of this thesis formulated two hypotheses: 1) Students’ listening ability and their oral English production ability are correlated. 2) Teachers who bring listening and audio-visual materials into oral English class are likely to have better teaching results.Krashen’s Comprehensive Input Hypothesis is the theoretical foundation of the author’s research. The author studies the nature of listening and speaking, by doing so she points out the effects of listening on improving students’ oral English from two broad aspects.This thesis aims at making a quantitative analysis on the effects of listening on speaking for college students. With the help of SPSS 11.5 software, a quantitative computerized analysis on this research hypothesis is made. Moreover, a quantitative analysis on correlation between listening and speaking is also made.The result shows that listening and speaking ability are closely related, and listening does have positive effects on improving college students’ oral English.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".