Improving written-expression curriculum-based measurement feasibility with automated writing evaluation programs.
Bibliographic record
Abstract
Automated writing evaluation programs have emerged as alternative, feasible approaches for scoring student writing. This study evaluated accuracy, predictive validity, diagnostic accuracy, and bias of automated scores of Written-Expression Curriculum-Based Measurement (WE-CBM). A sample of 722 students in Grades 2-5 completed 3-min WE-CBM tasks during one school year. A subset of students also completed the state-mandated writing test the same year or 1 year later. Writing samples were hand-scored for four WE-CBM metrics. A computer-based approach generated automated scores for the same four metrics. Findings indicate simpler automated metrics such as total words written and words spelled correctly, closely matched hand-calculated scores, while small differences were observed for more complex metrics including correct word sequences and correct minus incorrect word sequences. Automated scores for simpler WE-CBM metrics also predicted performance on the state test similarly to hand-calculated scores. Finally, we failed to identify evidence of bias between African American and Hispanic students associated with automated scores. Implications of using automated scores for educational decision making are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.031 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".