The revision of syntactic errors related to complex sentences in French L1: strategies of secondary school advanced writers
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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