Does <scp>CBM</scp> maze assess reading comprehension in 8–<scp>9‐year</scp> olds <scp>at‐risk</scp> for dyslexia?
Bibliographic record
Abstract
Recent research has reported the word-level, code-related focus of curriculum-based measures (CBM) of reading comprehension such as Maze (Muijselaar et al., 2017) with typically developing readers, but research has yet to examine whether this finding also applies to children at-risk for dyslexia. We administered a collection of cognitive, linguistic, CBM, and norm-referenced measures to children whose word reading and decoding fluency fell below the 25th percentile and were, therefore, considered at-risk readers. We found that language comprehension contributed additional variance beyond decoding (fluency and accuracy measures) to reading comprehension as assessed by the WIAT-III, but that decoding explained the most variance in children's performance on the CBM Maze task (vis à vis the simple view of reading). The findings have practical implications to the use of CBM Maze as a formative assessment with children at-risk for dyslexia and elucidate the need for additional or alternative assessments to capture the reading comprehension construct.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".