Validity of Automated Learning Progress Assessment in English Written Expression for Students with Learning Difficulties
Bibliographic record
Abstract
We evaluated the validity of an automated approach to learning progress assessment (aLPA) for English written expression. Participants (n = 105) were students in Grades 2–12 who had parent-identified learning difficulties and received academic tutoring through a community-based organization. Participants completed narrative writing samples in the fall and spring of one academic year, and some participants (n = 33) also completed a standardized writing assessment in the spring of the academic year. The narrative writing samples were evaluated using aLPA, four hand-scored written expression curriculum-based measures (WE-CBM), and ratings of writing quality. Results indicated (a) aLPA and WE-CBM scores were highly correlated with ratings of writing quality; (b) aLPA and more complex WE-CBM scores demonstrated acceptable correlations with the standardized writing subtest assessing spelling and grammar, but not the subtest assessing substantive quality; and (c) aLPA scores showed small, statistically significant improvements from fall to spring. These findings provide preliminary evidence that aLPA can be used to efficiently score narrative writing samples for progress monitoring, with some evidence that the aLPA scores can serve as a general indicator of writing skill. The use of automated scoring in aLPA, with performance comparable to WE-CBM hand scoring, may improve scoring feasibility and increase the likelihood that educators implement aLPA for decision making.
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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 | 0.036 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".