An Empirical Study on the Effectiveness of Multimedia Annotation to the News Listening Comprehension
Bibliographic record
Abstract
With the continuous progress of science and technology, computers are more widely used in English teaching. In most English classes, computer aided learning and teaching has become an indispensable part of classroom activities. On the other hand, enhancing language skills through multimedia technologies become one of the scholars’ concerns. English tests like College English Test (CET) is very popular among the Chinese mainland English learners, to whom the listening part is always the hardest section. Accordingly, how to improve their listening comprehension in the news section leaves us a hot debate in nowadays. For this purpose, through the investigation of how the news listening comprehension is subject to different modes of multimedia annotations, the paper tries to find out the correlation between the effectiveness of multimedia glossaries and listening comprehension of the Chinese mainland EFL learners and hopefully the study could inspire the language educators and other related professions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".