The influence of peer assessment on students’ writing scores in descriptive text
Bibliographic record
Abstract
This study investigates the influence of peer assessment on students’ writing scores in descriptive texts within the context of English as a Foreign Language (EFL) instruction. The importance of enhancing descriptive writing skills is underscored by its critical role in effective communication, particularly in language learning environments where detailed observation and expressive clarity are essential. While existing research has demonstrated the benefits of peer assessment for various writing genres, limited attention has been given to its application in descriptive writing and to students’ subjective experiences during the feedback process. This research addresses this gap by focusing specifically on how peer assessment shapes students’ revision behaviors, perceptions, and writing development in descriptive genres. Employing an exploratory research design, the study involved 24 second-year university students enrolled in an English Education program. Data were collected through pre- and post-writing tasks, structured peer assessment sheets, and guided reflections from participants. The analysis combined textual comparison of writing samples, thematic interpretation of feedback and reflection data, and quantitative examination of score changes, allowing for a comprehensive understanding of both performance outcomes and learners’ cognitive and emotional responses. The findings reveal significant improvements in students’ descriptive writing scores following peer assessment. Participants reported increased awareness of descriptive features, more strategic revision practices, and positive perceptions of peer feedback’s role in their learning process. The study highlights that peer assessment not only enhances writing performance but also fosters critical reflection and learner autonomy. These findings suggest that incorporating structured peer review processes can be an effective pedagogical strategy in EFL writing instruction, especially for genres demanding detailed and expressive language. The implications emphasize the need for educators to adopt multifaceted assessment approaches that integrate peer feedback to promote deeper engagement, independent thinking, and ongoing improvement in descriptive writing skills.
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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 | 0.007 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".