P. Shawn Irvin

Accommodations in Digital Interactive STEM Assessment Tasks: Current Accommodations and Promising Practices for Enhancing Accessibility for Students With Disabilities

In this article, we describe current research findings on assessment accommodations and universal design within the context of emerging interactive digital assessment tasks that employ simulations such as in science, technology, engineering, and mathematics (STEM). STEM education in many classrooms now includes digitally based activities such as science simulations and virtual laboratories that have been shown in some cases to promote learning gains. When such technologies are used in STEM assessments, a major challenge is to ensure assessments are accessible so all students can show what they know and can do. Federal laws and regulations including the Individuals with Disabilities Education Act, Elementary & Secondary Education Act, and Americans with Disabilities Act require that students with disabilities (SWD) be provided an opportunity to participate in educational programing and services available to nondisabled peers. In addition to implementing principles of universal design in assessment contexts, reasonable accommodations must be afforded to ensure accessibility. This article focuses on universal design and accommodations where the STEM construct is not adjusted or modified. Here, we employ synthesis of the research literature to document accessibility recommendations and practices around interactive assessment tasks, especially in STEM. We illustrate with an example and highlight directions that future development might take. The intention is to inform educators, school administrators, state and local policy makers, and assessment developers on the availability and use of accommodations in interactive assessment contexts such as simulation, and what is needed to ensure appropriate accessibility for SWD.

Modeling Reading Growth in Grades 3 to 5 With an Alternate Assessment

Modeling growth for students with significant cognitive disabilities (SWSCD) is difficult due to a variety of factors, including, but not limited to, missing data, test scaling, group heterogeneity, and small sample sizes. These challenges may account for the paucity of previous research exploring the academic growth of SWSCD. Our study represents a unique context in which a reading assessment, calibrated to a common scale, was administered statewide to students in consecutive years across Grades 3 to 5. We used a nonlinear latent growth curve pattern-mixture model to estimate students’ achievement and growth while accounting for patterns of missing data. While we observed significant intercept differences across disability subgroups, there were no significant slope differences. Incorporating missing data patterns into our models improved model fit. Limitations and directions for future research are discussed.