Evaluating Elementary Students' Response to Intervention in Written Expression
Bibliographic record
Abstract
Data-based individualization (DBI) is increasingly recognized as an effective service delivery framework to improve outcomes for students with or at risk of learning disabilities. To implement DBI, efficient and repeatable assessments of academic skills, with evidence of reliability and validity, are needed to determine when to modify academic interventions to improve student outcomes. Although such assessments are readily available in reading, developing similar assessments for written expression has been challenging. In this presentation, we will present an overview of findings from three studies addressing (a) the number and duration of writing samples that are needed for reliable estimation of student writing skill and (b) the potential for automated text evaluation to improve scoring feasibility and the convergent validity of student skill estimates. Two studies are based on writing samples from elementary students without learning disabilities in grades 2 through 5, and the third study is based on writing samples from students with learning disabilities in grades 1 through 12. Collectively, the findings provide preliminary information on writing sample assessment and scoring procedures that can give teachers good enough data to make defensible decisions about student progress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".