Curriculum-based measurement norming for reading fluency and written expression for French immersion students in School District #57
Bibliographic record
Abstract
Standardized tests are not always appropriate to assess French Immersion students.In School District #57, Learning Assistance (L.A.) teachers identified the need for an easy, inexpensive and reliable test.Curriculum-Based Measurement was a logical choice as it is directly related to classroom materials and instruction, and it is widely used in the English program to assess reading fluency, written expression and basic mathematics skills.The purpose of this project was to develop French CBM probes for reading fluency and written expression, and to develop local norms for the French Immersion program.The specific measures selected were Words Read Correctly, Total Words Written and Words Spelled Correctly.Norming tables were created with the data obtained during three norming periods.These tables will permit L. A. teachers and classroom teachers to assess and monitor students' progress efficiently and inexpensively.This report explains in detail the steps taken to develop the reading fluency and the written expression probes, the administration procedures and the scoring rules.It also verifies the reliability and the stability of the probes over time.The various probes are shown to be equivalent within grade.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".