Peer Editing in French Using Digital Tools: A Micro-Analysis of Learner-Computer Interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it