Longitudinal Analysis for Students With Significant Cognitive Disabilities: Looking Back in Order to Look Forward

• In Event: Alternate Assessments: Looking Back, Looking Forward, Looking Around

Abstract

Alternate assessments based on alternate achievement standards (AA-AAS) are a central component of statewide accountability systems. The population of students taking AA-AAS is small and diverse, and multiple test designs and formats are administered across the country. This study investigates student performance through a longitudinal analysis of assessment results from multiple states with varying assessment formats, test designs, participation criteria, and content/performance standards for students. One focus is the use of traditional and non-traditional matching to determine the best ways to track students over time.
The current study extends the work of van den Heuvel, Hansen, and Ilangakoon (2011), focusing on different methods of matching students, examining students who “fall” out of the data, and including an additional year of data. Given that many “traditional” methods don’t apply well to AA-AAS there is an increased need for such research.
Data from multiple states are examined. The states vary in relation to their assessment design. Four years of data for two content areas (math and reading) from each state are examined.
We perform traditional and non-traditional matching of student records. This is done because for this population of students, who will likely be enrolled within the public school system through age 21, there is evidence of “stalling,” where students remain in a particular grade for more than one year. Traditional matching means that a student would have been in grade 3 in year 1, grade 4 in year 2, grade 5 in year 3, and grade 6 in year 4. Non-traditional matching, for our purposes, allows for and accommodates “stalling,” such that a student could illustrate different grade progressions such as: grade 3 in year 1, grade 4 in year 2, grade 4 in year 3, grade 5 in year 4.
The traditional matching is complete and the analysis of the non-traditional matching is currently underway. Initial conclusions illustrate much variation in the participation counts (number of students) across included states, with one state having nearly double the student population of other states. Secondly, it is apparent that for all included states, male students comprise a majority of the students participating in the AA-AAS. Third, there are high percentages of students that can be tracked over time within each state assessment system for the AA-AAS. Fourth, the findings show that there isn’t a pattern of Matched or Unmatched students demonstrating significantly higher scores than their counterparts. Finally, there are different percentages of students who are classified at each reported performance level across states, which could have policy implications for states moving towards common assessments.
There is a lack of studies for the AA-AAS population examining results across states, across test designs, and over time. The current study provides significant research with this vital cross-examination. Ensuring that all consortia participants have clearly defined policies and implementation rules will be vital to the success and comparability of results. Further, with the addition of looking at non-traditional matching we strive to present more unique ways for states/consortia to access valuable information about their students.

Authors

• Jill R. van den Heuvel, Alpine Testing Solutions

• Mary A. Hansen, Robert Morris University

• Cristina Ilangakoon, McGraw-Hill Companies