Norming of CBM reading and writing and DIBELS instruments for School District No. 57 (Prince George)
Bibliographic record
Abstract
This study explored the development of a series of local norming Tables for Curriculum Based Measurement (CBM) reading and writing measures and Dynamic Indicators of Basic Early Literacy Skills (DIBELS) for use in Grades Kindergarten through 7 of School District 57 (Prince George). A total of 2420 students from 44 elementary schools participated in a total of three testing sessions that took place in the fall, winter and spring of the 2002/2003 school year. The method of sampling and data collection was explained. The quality of the data set was evaluated. Stability and equivalence coefficients were calculated for these measures. Equivalence of the probes used for both reading and writing subtests were assessed using Analyses of Variance procedures. A series of norm tables for Grades 1 to 7 for the fall, winter, and spring testing periods were generated for CBM measures entitled Words Read Correctly, Total Words Written, and Words Spelled Correctly. A series of norm tables for Grades 1 and Kindergarten were generated for DIBELS measures which included Letter Naming Fluency, Nonsense Word Fluency, Initial Sound Fluency, Phoneme Segmentation Fluency and Oral Reading Fluency. These analyses indicate that the CBM and DIBELS measures possess the technical qualities necessary for their use as intended by School District 57. The increases in the CBM norm values over their 1996 values illustrate the wisdom of the completion of this renorming study in 2003 and more generally the need for renorming studies to be done on a regular basis.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".