The Potential for Automated Text Evaluation to Improve the Technical Adequacy of Written Expression Curriculum-Based Measurement
Bibliographic record
Abstract
Written-expression curriculum-based measurement (WE-CBM) is used for screening and progress monitoring students with or at risk of learning disabilities (LD) for academic supports; however, WE-CBM has limitations in technical adequacy, construct representation, and scoring feasibility as grade-level increases. The purpose of this study was to examine the structural and external validity of automated text evaluation with Coh-Metrix versus traditional WE-CBM scoring for narrative writing samples (7-min duration) collected in fall and winter from 144 second- through fifth-grade students. Seven algorithms were applied to train models of Coh-Metrix and traditional WE-CBM scores to predict holistic quality of the writing samples as evidence of structural validity; then, external validity was evaluated via correlations with rated quality on other writing samples. Key findings were that (a) structural validity coefficients were higher for Coh-Metrix compared with traditional WE-CBM but similar in the external validity analyses, (b) external validity coefficients were higher than reported in prior WE-CBM studies with holistic or analytic ratings as a criterion measure, and (c) there were few differences in performance across the predictive algorithms. Overall, the results highlight the potential use of automated text evaluation for WE-CBM scoring. Implications for screening and progress monitoring are discussed.
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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 | 0.041 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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".