Validity of Automated Text Evaluation Tools for Written-Expression Curriculum-Based Measurement: A Comparison Study
Bibliographic record
Abstract
Existing approaches to measuring writing performance are insufficient in terms of both technical adequacy as well as feasibility for use as a screening measure. This study examined the validity and diagnostic accuracy of several approaches to automated essay scoring as well as written expression curriculum-based measurement (WE-CBM) to determine whether an automated approach improves technical adequacy. A sample of 140 fourth grade students generated writing samples that were then scored using traditional and automated approaches and examined with the statewide measure of writing performance. Results indicated that the validity and diagnostic accuracy for the best performing WE-CBM metric, correct minus incorrect word sequences (CIWS) and the automated approaches to scoring were comparable with automated approaches offering potentially improved feasibility for use in screening. Averaging scores across three time points was necessary, however, in order to achieve improved validity and adequate levels of diagnostic accuracy across the scoring approaches. Limitations, implications, and directions for future research regarding the use of automated scoring approaches for screening 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 | 0.050 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".