Research on College English Expansion Classroom Based on Mobile Learning in the Era of New Media
Bibliographic record
Abstract
With the rapid development and popularity of intelligent and mobile devices, the new media era has entered an unprecedented climax, and various products have emerged, such as networked media platforms, digital television, digital newspapers and magazines, etc. The education model has also undergone great changes and breakthroughs. Traditional teaching philosophies, methods and resources are relatively homogeneous, with some teachers focusing on the instrumental aspects of English to the detriment of its humanistic aspects, thus limiting the enhancement of students' English literacy. In addition, the atmosphere of English learning and students' motivation also greatly affects the efficiency of English learning. This article uses mobile learning as a method to explore its effects on the extended English classroom at university, with the aim of better helping university English teachers to build an effective extended English learning classroom. Using some common English learning indicators, the aim of the article is to investigate and analyse the effectiveness of mobile learning as a way for students to develop their English language skills. The results of the experiment showed that students' ability to learn English independently at university increased after using mobile learning, with a 1.89% increase for English majors and a 5% increase for non-English majors who chose 'often learn independently'. In addition, the proportion of students who were not sure whether they would study independently dropped by 1.11% and 0.22% respectively, indicating that students have become more clear about their goals and orientation after mobile learning. All in all, mobile learning based on new media platforms has a beneficial effect on the expansion of English classes in universities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".