Rétroaction par les pairs par l'entremise de blogues : Perceptions et pratiques d'étudiants universitaires avancés d'anglais langue seconde
Bibliographic record
Abstract
This multi-case study investigates the perceptions and practices of blog-mediated peer feedback in the context of an academic writing class of advanced ESL adult learners. The study aims to examine the linguistic errors commonly made by these learners, explore how they provide feedback to their peers through blogs, analyze their responses to the feedback received, and investigate their perceptions of this feedback approach. \nThe study was conducted at a francophone Canadian university, in the context of a mandatory academic writing course within the Teaching English as a Second Language (TESL) program. The study follows a multiple-case study design combining qualitative and quantitative data collection methods, including analysis of written productions, blog-mediated peer feedback, semi-structured interviews, and a demographic questionnaire. The findings highlight that sentence structure and spelling are the most frequent error types among advanced adult ESL learners. Furthermore, they reveal that the predominant feedback type preferred by these learners is direct error correction with comments, followed by direct error correction without comments. In terms of their revisions learners not only aligned these with their peer comments but also made correct substitutions. As for learners’ perceptions, although all the participants showed a positive attitude towards blog-mediated peer feedback, emphasizing its user-friendliness and convenience, some learners expressed concerns about their peer’s competence to provide feedback. The results of this study contribute to the understanding of the benefits and challenges of using blog-mediated peer feedback as a pedagogical tool in ESL writing classrooms. Furthermore, it provides valuable insights for instructors of advanced ESL learners in higher education regarding the types of errors their students tend to make when writing in academic contexts as well as how they integrate the feedback provided by their peers.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".