Students’ perceptions of online peer feedback in process-oriented L2 writing: A qualitative inquiry
Bibliographic record
Abstract
Peer feedback has been of paramount importance in second language (L2) writing given that L2 learners especially need the facilitative role of feedback in improving their writing skills. Despite recent research highlighting the benefits of peer feedback in L2 writing, there is a call for more qualitative studies situated in classroom contexts to explore the continuous flow of peer feedback in a social community including technology-enhanced interactive social settings. This case study takes the students at a university in Turkey as the center of the peer feedback process and explores how L2 writers (n = 35) using student blogs for online peer evaluation in the naturalistic setting of an academic writing course perceive online peer feedback in L2 writing. This qualitative study adopted a case study design using the data from students’ essays (210 essays), blog-mediated peer comments (on 35 student blogs), and self-reflection papers (105 reflections). The data was analyzed through Braun and Clarke’s inductive thematic analysis. The analysis resulting in four themes revealed that peer feedback experienced in an online writing community improved students’ ability to perceive and use feedback effectively by creating opportunities for a shared understanding of peer feedback and establishing a writing community and also enhanced students’ critical thinking skills as an essential aspect of the writing process. This study provides insights into the student experience of online peer feedback in L2 writing, highlighting the importance of creating a supportive and collaborative learning environment to foster effective feedback practices and enhance the students’ L2 writing skills. • More qualitative studies are needed to explore peer feedback in technology-enhanced classroom settings. • This study examines Turkish students' perspectives on online peer feedback in L2 writing. • Online peer feedback in L2 writing benefits higher education students by expanding their roles as L2 writers. • Online peer feedback enhances critical thinking, social interaction, and community building in L2 writing. • This study emphasizes online peer feedback in L2 writing and provides insights into students' views on the practice.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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 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".