Efficacy of WeChat Instant Messaging Platform for Peer Feedback on Undergraduate Students’ English Academic Writing Performance: A Systematic Literature Review
Bibliographic record
Abstract
Peer feedback has become a crucial element in the field of English as a second/foreign language (ESL/EFL) writing. There has been a notable scholarly interest in the integration of peer feedback in English writing instruction. Among Chinese undergraduate students, there is a growing tendency to use instant messaging systems, such as WeChat, for peer feedback. WeChat distinguishes itself from other platforms by its extensive usage in China, particularly owing to its user-friendly features. However, there is a lack of systematic research in the current literature to explore the effectiveness of using WeChat for peer feedback in English writing. In particular, there is a lack of research on five aspects of students’ English writing performance when using WeChat as a platform for peer feedback, such as content and idea, coherence and cohesion, grammar and form, word choice and vocabulary, and organization and structure. This article aims to provide an overview of the investigation of the effectiveness of using WeChat as an instant messaging platform through a literature review. The authors adhered to predetermined criteria for what literature were included and excluded. Ultimately, the authors reviewed a collection of 30 research articles from the year 2011 to 2023 that are directly related to the topic at hand. This review encompasses an evaluation of the research designs, findings, discussions, and gaps in research identified in these articles. This review offers a comprehensive comprehension of the present status of research in this field. Moreover, it emphasizes the need for additional academic investigation, specifically targeting the task of filling the recognized gaps in knowledge. Furthermore, the conclusion highlights the necessity of further studies, which needs to explore the long-term effects of incorporating WeChat and peer feedback on enhancing students’ writing performance. This encompasses various aspects, such as content and idea, coherence and cohesion, word choice and vocabulary, organization and structure, as well as grammar and form.
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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".