Improving the mastery of relative clause in French L1 secondary classes: the effects of an intervention based on verbal interactions on written syntactic structures
Bibliographic record
Abstract
Using the accurate relative pronoun (RP) in a formal writing task presents challenges for writers since they seem to be influenced by forms used in the popular oral variety of French which are far from the linguistic norm (Blanche-Benveniste, 2010). Studies describing the teaching of the relative clause (RC) in the secondary classroom have highlighted the problems encountered by students not only with handling this grammatical object, but also with using their grammatical knowledge in revising their text (Dolz & Schneuwly, 2009). However, to our knowledge, no study has yet been conducted to conceive and test an intervention for teaching RCs in French L1 classes. Based on theoretical and empirical work converging toward the fostering of sustained verbal interactions throughout grammatical and revision instruction, a series of lessons was implemented with 52 grade nine students enrolled in a French course (Montreal, Canada). Pretest and posttest texts were analysed in terms of RC frequency, usage and accuracy. While no difference was found in the general frequency of RCs, results show a significant increase in the use of complex RPs. Students, especially the weaker ones, also make significantly fewer mistakes overall on RPs and also on complex RPs. These results could indicate that certain structures associated with complexity and formal register are used more frequently and more accurately during written production after our intervention. Our results contribute to the ongoing discussion on the complementarity between direct grammar instruction and writing and revision instruction and their positive impact on students' syntactic constructions in texts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".