Introducing a Computer-Adaptive Testing System to a Small School District
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
A computer-adaptive test (CAT) is a relatively new type of technology in which a computer program “intelligently” selects and presents questions to examinees according to an evolving estimate of achievement and a prescribed test plan. A well written CAT can be expected to efficiently produce student achievement estimates that are more accurate and more meaningful than a typical teacher-generated paper and pencil (P&P) test with a similar number of questions. Although this method of testing sounds good in theory, many schools and districts are waiting for positive examples of practical applications and observable benefits before adopting a CAT. This chapter begins by describing the essential elements of meaningful measurement in education and the features of a typical CAT. Next, we describe the Measures of Academic Progress (MAP) system of the Northwest Evaluation Association (NWEA; 2004) and observations made during the introduction of this system into a small semirural school district. Finally, as independent observers, we provide a set of recommendations to help guide other districts as they consider the potentials of implementing a CAT system to guide instruction within their schools.
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.
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 | 0.000 | 0.000 |
| 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 | 0.002 | 0.001 |
| 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