Mining achievement data to guide policies and practices on assessment options

Mining achievement data to guide policies and practices on assessment options Scott Marion Brian Gong Mary Ann Simpson National Center for the Improvement of Educational Assessment NCEO Teleconference February 6, 2006

Study Questions • Are most special education students low performing? • Are most low-performing students in special education? • Is there a relationship between disability code and performance on statewide assessments?

Method • Recent assessment data, Math and ELA from 5 states (2003, 2004) • States are geographically distributed, but no large states are included • Detailed IDEA classification available for students from 2 of these states • Examine student performance by special education status, all 5 states • Examine student performance by detailed IDEA classification, 2 states

Analysis • In the spirit of Tukey, we used very simple descriptive statistics and graphical representations of the results • The following graph was done with a crosstab of scale score values by special education status and then transformed into a stacked histogram

Vertical Lines shows lowest 3 percent & NCLB Proficient

What does the chart mean? • Note that special education students are represented at all scale scores in the distribution • Importantly, a significant number of general education students are found in the lowest scoring three percent of students

Notes from the detailed analyses • Significant variability in percent of special education students scoring proficient by disability category • Notable variability across the two states in percent proficient within the same disability category • Why? • Is likely due to different definitions of proficient? • Is it due to different ways of classifying students into disability categories (note the percent of special education students in various disability categories across states)

Additional considerations • How do we define/classify the students who should be eligible for the 2% flexibility? • What do these analyses tell us? • Preliminary work suggests that the lowest scoring 2% of students on the general assessment are not a stable group over time • Longitudinal analyses will help provide insight regarding this issue • Disability categories do not provide a useful means of determining eligibility for many reasons, including variability of performance and variability of classifications

Mining achievement data to guide policies and practices on assessment options

Mining achievement data to guide policies and practices on assessment options

Presentation Transcript

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