Bibliographic record
Abstract
To improve students’ reading ability is one of the fundamental requirements for English teaching for English majors. However, some English majors are not interested in reading English and lack motivation to learn it. Even teachers may lose enthusiasm to teach them English. As a result, teaching English reading is inefficient.Application of multimodality in teaching English has attracted many researchers’ attention; the author applies multimodality to teaching English reading and attempts to answer the following question: Is the application of multimodality to teaching English reading effective?An experiment is carried out in two parallel classes for a whole term. In the experimental class, multimodality is applied in teaching reading and teaching procedures are all designed according to the theory of elements of designing multimodality while in the control class, ordinary multimedia teaching is applied. The data of pre-test, reading quizzes, a post-test are analyzed by SPSS 16.0. The research finds out that on the one hand, the application of multimodality in teaching reading is indeed effective. On the other hand, multimodal teaching is more popular among English majors for it can help activate classroom atmosphere, inspire students’ motivation to read after class and build up their confidence in learn English, especially English reading. The author also provides the suggestions for English teaching based on the research findings.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".