Improving students’ punctuation: classroom experimentation and results in texts by primary and secondary school students in Quebec
Bibliographic record
Abstract
The texts of primary and secondary school students show a high proportion of written errors related to punctuation (Boivin and Pinsonneault, 2018, among others). Yet punctuation instruction often offers students only limited opportunities to improve their control of sign usage in a writing context (Riverin and Dufour, 2018). This article presents a quasi-experimental study involving 16 French classes from cycle 3 of primary school and cycle 1 of secondary school in Quebec (age 10 to 13). Students in the experimental groups carried out a sequence of 20 activities implementing three innovative didactic devices, designed during a first phase of the research, in collaboration with the participating teachers. The aim was to improve punctuation and syntax in writing. We will present results drawn from the analysis of short texts written at pre-test and post-test (two texts at each time: one descriptive and one narrative) in the experimental and control groups, in relation to success variables in segmentation into graphic sentences (capital letter & full stop), and in punctuation within graphic sentences and syntactic sentences (clauses, i.e. subject + predicate (+ sentence adverbial(s)), for some comma usage rules.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".