Topics: Item response theory
MAP Growth universal screening benchmarks: Establishing MAP Growth as an effective universal screener.
The report documents the process NWEA followed to determine and validate the cut scores for fall, winter, and spring that can be used to identify students in Grades K–8 who have severe learning difficulties and need intensive intervention in reading and mathematics.
Approaches to test score use and test purpose lack the well-developed methodological guidelines and established sources of evidence available for intended score interpretation. We argue in this paper that this lack fails to reflect the ultimate purpose of a test score—to help solve an important problem faced by intended test users.
This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria, such as AIC, BIC.
By: Kang Xue, Laine Bradshaw
Topics: Measurement & scaling
To avoid the subjectivity of having a single person evaluate a construct of interest (e.g., a student’s self-efficacy in school), multiple raters are often used. This study provides a model for estimating growth in the presence of multiple raters.
Comparing different response time threshold setting methods to detect low effort on a large-scale assessment
This study uses reading test scores from over 728,923 3rd–8th-grade students in 2,056 schools across the US to compare threshold-setting methods to detect noneffortful item responses. and so helps provide guidance on the tradeoffs involved in using a given method to identify noneffortful responses.
Topics: School & test engagement
This study identifies students’ academic trajectories in the middle grades relative to a set of college readiness benchmarks. We apply math and reading college readiness benchmarks to rich longitudinal data for more than 360,000 students across the nation. Student-level and school-level demographic characteristics significantly predict academic trajectories.