The revision of syntactic errors related to complex sentences in French L1: strategies of secondary school advanced writers
Bibliographic record
Abstract
This article presents a description of the revision strategies targeting complex sentences of 16 secondary school advanced writers (15-17 years old) in the context of French L1 instruction. As the literature indi-cates, most errors in students' texts are syntactic errors (Boivin & Pinsonneault, 2018), and revising them entails a heavy cognitive load (Roussey & Piolat, 2008). We conducted a multiple case study among these advanced writers to identify their detection, diagnosis and correction strategies targeting syntactic problems. Thinking-aloud (Ericsson & Simon, 1993, Hayes & Flower, 1980), they revised one individual text and one experimental text containing 22 different syntactic errors related to complex sentences. We focused on the revision strategies leading to accurate changes. Our results show that advanced writers make a very limited use of detection strategies. Their diagnosis strategies are mainly reflections, grammaticality judgments and rereadings. Students with high rates of accurate changes in the experimental text use fewer diagnosis strategies than those with average rates. Self-questioning appears to be a strategy most used by students with high rates of accurate changes. The corrections are generally precise and made immediately after a problem is detected. Looking at individual cases, we also present salient pro-files based on the students' posture toward revision and syntax.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".