Assessing student achievement over multiple years is complicated by students' memberships in shifting upper-level nesting structures. These structures are manifested in (1) annual matriculation to different classrooms and (2) mobility between schools. Failure to model these shifting upper-level nesting structures may bias the inferences…

Medical educators and programs are deeply interested in understanding and projecting the longitudinal developmental trajectories of medical students after these students are matriculated into medical schools so appropriate resources and interventions can be provided to support students' learning and progression during the process. As students have…

The computational model of school achievement represents a novel approach to theorizing school achievement, conceptualizing educational interventions as modifications to students' learning curves. By modeling the process and products of educational achievement simultaneously, this tool addresses several unresolved questions in educational…

Assessing student achievement over multiple years is complicated by students' annual matriculation through different classrooms. The process of matriculation, or annual classroom change, threatens the validity of statistical inferences because it violates the independence of observations necessary in a regression context. The current study…

Considerable interest lies in the growth in educational achievement that occurs over the course of a child's schooling. This paper demonstrates a simple but effective approach for the comparison of growth rates, drawing on a method first proposed some 80 years ago and applying it to data from the Australian National Assessment Program. The…

Studies on utility value, metacognitive strategies, and achievement have usually examined these variables in a static manner. However, each of these variables changes across time and the relationships among them are dynamic. Hence, studies that examine changes in individual trajectories (change in each variable over time) and concurrent…

The number of low-performing schools has drastically increased since COVID-19. During the 2018-2019 school year, there were 488 low-performing schools (North Carolina Department of Public Instruction, 2024). The number increased to 736 schools during the 2023- 2024 school year, a 50.8% increase (North Carolina Department of Public Instruction,…

The MAP® Growth™ theory of action describes key features of MAP Growth and its position in a comprehensive assessment system. The basic premise of the theory of action is that all students learn when MAP Growth is situated in a comprehensive assessment system and used for its intended purposes to yield information about student learning and enable…

Although there is recognition that there may be differential outcomes for groups of students within schools, examination of outcomes for subgroups presents challenges to researchers and policymakers. It complicates analytic procedures, particularly when the number of students per school in the subgroup is small. We explored five alternatives for…

We evaluate the feasibility of estimating test-score growth for schools and districts with a gap year in test data. Our research design uses a simulated gap year in testing when a true test gap did not occur, which facilitates comparisons of district- and school-level growth estimates with and without a gap year. We find that growth estimates…

The Colorado Growth Model (CGM) was developed jointly by the Colorado Department of Education (CDE), the Technical Advisory Panel for Longitudinal Growth (TAP), and the National Center for the Improvement of Educational Assessment (NCIEA). Its development was required by state statute (SB09-163) and assigned to the Technical Advisory Panel. The…

With the shift from No Child Left Behind (NCLB) to Every Student Succeeds Act (ESSA), accountability models are being changed. Given the past 15 years of reporting on student subgroups and 10 years using various growth models, accountability systems can now be better informed. In this study, we analyze identification and services of students with…

We used the Stanford education data archive (SEDA) data to examine the heterogeneity among urban school districts in the United States. The SEDA 2.1 includes data sets on students' mathematics (Math) and English language arts (ELA) achievement from 2008 to 2014 at the district level. Growth mixture modeling was used to uncover the underlying…

Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and…

States can and should continue to measure student growth in 2021. Growth data will be crucial to understanding how school closures due to COVID-19 have affected student progress and what supports they will need to get back on track. Education leaders will also need growth data to ensure that any recovery efforts are equitable as well as effective…