Robust Regression for Slope Estimation in Curriculum-Based Measurement Progress Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although ordinary least-squares (OLS) regression has been identified as a preferred method to calculate rates of improvement for individual students during curriculum-based measurement (CBM) progress monitoring, OLS slope estimates are sensitive to the presence of extreme values. Robust estimators have been developed that are less biased by extreme values; however, the performance of robust estimators in the short data streams typical of CBM progress monitoring is unknown. The purpose of the current study was to investigate bias and efficiency relative to OLS for several robust slope estimators on simulated CBM progress monitoring data. Data were generated at several combinations of series lengths (i.e., 7, 12, and 24 data points) and percentages of extreme value contamination (i.e., 0%, 15%, and 30% of data points). Results indicated that the robust slope estimates were substantially more efficient than OLS in the presence of extreme values. Potential uses of robust slope estimates for calculating students’ rates of improvement in CBM progress monitoring are discussed.
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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 | 0.003 | 0.002 |
| 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 | 0.000 | 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 it