Measuring annual growth using written expression curriculum-based measurement: An examination of seasonal and gender differences.
Bibliographic record
Abstract
The purpose of this study was to examine annual growth patterns and gender differences in written expression curriculum-based measurement (WE-CBM) when used in the context of universal screening. Students in second through fifth grade (n = 672) from 2 elementary schools that used WE-CBM as a universal screener participated in the study. Student writing samples were scored for production-dependent, production-independent, and accurate-production indicators. Results of latent growth models indicate that for most WE-CBM outcome indicators across most grade levels, average growth was curvilinear, with increasing curvilinearity on all indicators as grade level increased. Evidence of gender differences was mixed with girls having higher initial scores on all WE-CBM indicators except for total words written (second and third grades), correct minus incorrect writing sequences (fourth grade only), and percent correct writing sequences (second-fourth grades) where differences were not statistically significant. Despite differences in initial level, there were few gender differences in growth and limited overall between-student variability in linear slope. The results of this study extend research on annual patterns of growth and gender differences in WE-CBM by analyzing all 3 types of WE-CBM indicators, including upper elementary grades, and assessing skills more frequently (i.e., 4 to 5 times in 1 year) than in prior research on annual growth. The findings have implications for universal screening in WE-CBM and for understanding gender differences in writing performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".