Digital tools for the learning of grammatical revision with French L1 learners: Results from a systematic approach study
Bibliographic record
Abstract
French L1 students face difficulties in text revision, particularly in applying grammatical knowledge to their writing and navigating the complexities of French spelling. Online digital learning tools may offer valuable support in overcoming these challenges. What is the real potential of these tools for developing revision skills? To what extent do they help students apply their grammatical knowledge in writing? Based on a study (Arseneau & Geoffre, 2023), we present a systematic approach study that examined 126 digital tools for grammatical text revision in French as an L1, using three main variables: grammatical content, task type, and feedback. The results reveal that the majority of the surveyed tools (92.9%) focus on specific grammatical content (primarily grammatical spelling) and engage users in only one type of task (87.3%). Furthermore, the most crucial tasks for learning text revision –such as identifying and correcting errors– are surprisingly addressed by only a few tools. However, the feedback provided to learners is generally comprehensive, often combining various forms, including metalinguistic explanations or cues (76.2%). The results are discussed considering principles for revision and grammar instruction, along with a reflection on pedagogical implications and on what an “ideal tool” design might look like, potentially incorporating AI.
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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".