Effects of teacher-implemented explicit writing instruction on the writing self-efficacy and writing performance of 5th grade students
Bibliographic record
Abstract
Meta-analyses indicate that explicit writing instruction (EWI) is an effective method for improving student writing self-efficacy and writing performance. EWI relies on explicit instruction of writing strategies through modeling, scaffolding and self-regulation. Most EWI-based interventions have been conducted by researchers, generally with subgroups of students or on a one-on-one basis, and very few have been conducted in other languages than English. Our quasi-experimental study aims to address these limits by testing EWI’s effects when teachers themselves intervene using peer feedback during the writing of opinion letters. We used practice-based professional development to teach teachers how to use EWI, and compared two experimental conditions (EWI with and without peer feedback) to a control group (Business as Usual). A total of 483 French-speaking 5th grade students participated in the study. Results from repeated measure analyses showed that, with or without peer feedback, the EWI intervention produced better writing performance and higher self-efficacy compared to the control group. We discuss the role of EWI for writing performance and self-efficacy.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".