Validity Evidence for Progress Monitoring With Star Reading: Slope Estimates, Administration Frequency, and Number of Data Points
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
The increasing use of computerized adaptive tests (CATs) to collect information about students’ academic growth or their response to academic interventions has led to a number of questions pertaining to the use of these measures for the purpose of progress monitoring. Star Reading is an example of a CAT-based assessment with considerable validity evidence to support its use for progress monitoring. However, additional validity evidence could be gathered to strengthen the use and interpretation of Star Reading data for progress monitoring. Thus, the purpose of the current study was to focus on three aspects of progress monitoring that will benefit Star Reading users. The specific research questions to be answered are: (a) how robust are the estimation methods in producing meaningful progress monitoring slopes in the presence of outliers; (b) what is the length of the time interval needed to use Star Reading for the purpose of progress monitoring; and (c) how many data points are needed to use Star Reading for the purpose of progress monitoring? The first research question was examined using a Monte Carlo simulation study. The second and third research questions were examined using real data from 6,396,145 students who took the Star Reading assessment during the 2014-2015 school year. Results suggest that the Theil-Sen estimator is the most robust estimator of student growth when using Star Reading. In addition, it appears that five data points and a progress monitoring window of approximately 20 weeks appear to be the minimum parameters for Star Reading to be used for the purpose of progress monitoring. Implications for practice include adapting the parameters for progress monitoring according to a student’s current grade-level performance in reading.
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 | 0.000 | 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