Perceptions of Chinese University English Majors on the Use of WeChat as a Platform for Peer Feedback in English Writing
Bibliographic record
Abstract
Peer feedback is widely acknowledged as a valuable addition to English language learning, with several research confirming its various benefits in improving English writing skills (Liu & Edwards, 2018). WeChat is the main communication tool in China, especially among young people. However, its potential for peer evaluation in English writing has not been thoroughly explored especially for English language learning, and more specifically for giving and receiving peer feedback for English writing. Current research mostly centers on Chinese college students, neglecting English majors who have comparable difficulties in mastering English writing abilities (Gan et al., 2004). Therefore, this study aims to investigate the perceptions of Chinese English majors on using WeChat for peer feedback. Data was gathered using a structured questionnaire that was designed based on the Technology Acceptance Model (TAM). The results show that most respondents have a good attitude towards using WeChat for peer feedback. Respondents have positive views on the platform's effectiveness in enabling peer feedback, along with factors like output quality, results demonstrability, and perceived ease of use. Participants also positively view aspects, such as external control, behavioral intention, voluntariness, and perceived enjoyment in using WeChat for peer feedback in English writing. Integrating WeChat as a peer feedback platform is considered beneficial for enhancing the teachinglearning process, which is important for educators and curriculum designers. The researchers posit that the use of WeChat as a platform can facilitate peer feedback for students’ English writing learning as a second language teaching tool as it shows the potential to improve the effectiveness of English education whilst also promoting the use of technology outside the classroom for peer feedback and interactive English language learning.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".