NOUVELLES TECHNOLOGIES, NOUVELLES PRATIQUES D’ECRITURE ? Le point de vue d’étudiants universitaires en FLE(S)
Bibliographic record
Abstract
The digital revolution and the development of new technologies of communication are dramatically changing language learning practices but also writing practices, whether in one's mother tongue, in a second language or in a foreign language.In the framework of international research based on the development of writing ability among students of a second language or a foreign language in the university context, we have sought to better understand the way in which the relationship to writing (Barré de Miniac, 1997; Blaser and Chartrand, 2010) is constructed by these students in this new era.This article is intended to share the results of a survey conducted among some 200 students of French as a second language or a foreign language in four contrasting geographical and linguistic contexts.After a description of the ways in which students claim to use these new technologies at the university, we will analyze their use of ICTS when writing longer texts in an academic setting.Next, we will look into the functions served by the Internet at every step of the writing process, i.e. the different stages of planning, drafting and revising (Hayes et Flower, 1980).Finally, we will consider the kinds of tools used for the drafting of these various pieces of writing.
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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 | 0.041 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".