Examining performance indicators for a written expression test based on the curriculum
Bibliographic record
Abstract
This study aimed to investigate the relationships between students' word-level reading competency, verbal working memory, and performance on a curriculum-based narrative writing exam (N = 42; 23 males). Composition quality and CMIWS, an abbreviation that stands for correct minus incorrect word sequences, were the outcomes evaluated in this study. CMIWS is an abbreviation for "Creation of suitable text grammar and spelling." This is what the acronym means. The CMIWS scores and the total quality score did not strongly correlate. As a predictor of CMIWS, word reading ability fared significantly better than gender, grade level, the automaticity of handwriting, and working memory. This is because having a strong reading ability lowered the amount of variety in the data. Reading fluency remained a strong predictor of composition quality even when other factors, such as the writer's gender and the degree to which their handwriting was automatic, were considered. An updating task was used to evaluate working memory; however, the results did not indicate that it was a unique or significant predictor of CMIWS or composition quality. In addition, it was found that scores on the CMIWS varied depending on grade (the findings for grade 5 were higher than those for grade 4) and gender. Grade 5 scores were higher than grade 4 (girls had higher scores than males). Even though there is a correlation between automaticity in handwriting and both CMIWS scores and writing quality, automaticity in handwriting alone cannot be used to predict any of these measures. The findings support the hypothesis that the CMIWS indicator is sensitive to variations in the environment in which it is located. When evaluating children's writing abilities and developing instructional strategies for children who struggle with writing, it is essential to consider not only the children's reading fluency but also the amount of automaticity in their handwriting. As highlighted by these outcomes, the importance of taking this step cannot be overstated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".