A Study on the Mobile Learning of English and American Literature Based on Wechat Public Account
Bibliographic record
Abstract
This paper uses Edgar Dale’s Audio-visual Learning Theory and Jean Piaget’s Constructionist Learning Theory as the theoretical framework to conduct two control experimental tests and a questionnaire research to investigate students’ impression and expectations toward Wechat public account based mobile learning mode as well as its validity, advantages and shortcomings. The findings of the research are as follows: (1) students who use the WeChat public account based mobile learning mode can perform better in acquiring knowledge and passing examinations than non-users; (2) students’ mobile learning habits find a good basis for their acceptance of the new WeChat learning mode; (3) most students respond to the new WeChat learning mode with a positive attitude, and universities can introduce this mode into the course design; (4) this new WeChat learning mode helps students learn literature in the aspects of interest stimulation, ability improvement and literature content learning; (5) WeChat mobile learning is only an extension of the current form of learning and cannot replace the current traditional school education; (6) Mobile learning mode based WeChat public account is an important teaching aid for teachers in the aspects of information dissemination, interactive mode and updated contents; (7) The experience of improving the current mobile learning model of WeChat public account can be enriched from students’ feedback. It is hoped that WeChat mobile learning will efficiently activate students’ learning potentials and promote English and American literature teaching innovation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".