Peer Editing in French Using Digital Tools: A Micro-Analysis of Learner-Computer Interactions
Bibliographic record
Abstract
Abstract This paper describes a case study focused on the ways in which university-level learners of French as a second language collaborate during peer-editing sessions assisted by digital tools. The purpose of the study is to better understand users’ interactions with each other and with technologies at a micro level. Audio recordings and video screen captures of peer-editing sessions serve as a basis for our analysis of strategies deployed by 12 learners of French as a second language enrolled in an intensive intermediate grammar and writing course. Using a mixed-methods approach based on qualitative and quantitative data collected with five peer-editing groups, the study centres on processes in which participants engage to perform their tasks. The paper makes recommendations regarding task design and learners’ training for development of digital literacies. Résumé Cet article présente une étude de cas portant sur les stratégies utilisées par des apprenants de français langue seconde en milieu universitaire, lors de séances de correction des pairs assistées par des outils numériques. L’objectif de l’étude était de mieux comprendre, à un niveau micro, les façons dont les participants interagissaient entre eux, ainsi que d’identifier les interactions avec les outils numériques utilisés. Pour ce faire, nous avons eu recours à des enregistrements audio et à des captures d’écran de séances de correction des pairs pour analyser les stratégies mises en œuvre par ces étudiants inscrits dans un cours de grammaire et d’écriture de niveau intermédiaire. À partir des données d’ordre quantitative et qualitative recueillies auprès de cinq groupes d’apprenants, cette étude s’est concentrée sur les procédés auxquels avaient eu recours les participants pour accomplir la tâche. L’article offre des recommandations sur les conceptions de tâches et sur la formation à la littératie numérique.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".