In the visualizations in this exhibit, you can compare the performance and growth of various groups of high achievers to that of their peers over multiple years.

Some of our assumptions about the growth and performance of students from high-poverty schools relative to their peers from wealthier schools may be challenged in this data gallery, where you can explore how school poverty level interacts with student growth, college readiness, and college access.

This study examines the academic growth of 35,000 elementary and middle school students in 31 states—all of them high achievers within their own schools—over a three-year period.

In this study from the Thomas B. Fordham Institute, achievement trends from NWEA’s longitudinal growth database were used to track students who scored at or above the 90th percentile on this assessment in order to see if they maintained their high achievement.

The current investigative study uses a multiple-indicator, latent-growth modelling (MLGM) approach to examine the longitudinal achievement construct and its invariance for MAP Growth.

Modeling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modeling and teacher value-added modeling are complex.

This study examined the utility of response time-based analyses in understanding the behavior of unmotivated test takers. For an adaptive achievement test, patterns of observed rapid-guessing behavior and item response accuracy were compared to the behavior expected under several types of models that have been proposed to represent unmotivated test taking behavior.