Discourse Analysis of ESL Undergraduate Students’ Patterns of Interaction in an Online Peer Feedback Environment to Develop Writing Performance
Bibliographic record
Abstract
Academic writing is challenging for English as a Second Language (ESL) undergraduate students. One of the teaching strategies that language instructors use in teaching academic writing is by using peer feedback. However, in the ESL setting, many research has indicated mixed findings on the use of peer feedback. To contribute to the discussion, this qualitative study investigated the patterns of interaction between ESL students in an online peer feedback environment. The data were collected from six ESL undergraduate students through discourse analysis of their online peer feedback written interaction and content analysis of their essays. The findings revealed that two patterns of interaction emerged which include the expert/novice and dominant/passive pattern. However, there were none to small improvements among the students in terms of their writing performance. Although one of the patterns is collaborative (i.e., expert/novice), the quality and quantity of their feedback were lacking thus resulting in lower revisions and improvements made. The study recommends further research on the quality and quantity of peer feedback to understand better the role of online peer feedback in ESL students’ academic writing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".