The Third Wave of Longitudinal Data Systems: Data Partnerships
Author: Armstrong, Jane
Date: 2008-08-00
Type: Reports - Descriptive
Subjects: Partnerships in Education,Longitudinal Studies,Management Information Systems,Program Implementation,Design Requirements,Database Design,Information Networks,Student Records,Contracts,Information Policy,Progress Monitoring,Databases,Information Systems,Access to Information,Academic Achievement,Coding

States have made dramatic progress in the past few years in implementing longitudinal data systems (LDS) to improve student achievement. These systems collect and house student educational data that make it easy for policymakers and practitioners to access and use it for reporting, policymaking and decision making. So much has happened in states in a short period of time (just 2-3 years); now, some of these systems are entering their &quot;third wave&quot; of development and use. The first wave has been designing and implementing systems that include important student-level data that can be individually or collectively analyzed for decision and policymaking. The second wave has been increasing the range of students included in the aligned data systems in a state--moving from K-12 to P-20 data systems. The third wave-- emerging now in some states--is expanding the number of linked data sets used to inform policy-- that is, linking 
these systems to other databases, such as social services, financial information, and human resource data, and expanding the number of users by encouraging data partnerships. The Data Quality Campaign (DQC) is a strong national advocate for longitudinal data systems and encourages states to include &quot;ten essential elements&quot; to optimize these systems for educational decision making. They are: (1) A unique student identifier that connects student data across key databases across years; (2)  
Student-level enrollment, demographic and program participation information; (3) The ability to match individual students&apos; test records from year to year to measure academic growth; (4)  Information on untested students and the reasons they were not tested; (5) A teacher identifier system with the ability to match teachers to students; (6) Student-level transcript information, including information on courses completed and grades earned; (7) Student-level college readiness test scores; (8) Student-level graduation and dropout data; (9) The ability to match student records between the P-12 and postsecondary systems; and (10) A state audit system assessing data quality, validity and reliability. The capacity of state data systems to collect, analyze, and provide useful data to inform policymaker and educator decisions has dramatically increased since 2005 when the DQC first surveyed states about the capacity of their data systems. However, unless this data is analyzed deeply and used widely to help improve student achievement and outcomes, there will be little need to build robust longitudinal data systems. Some states have begun to work with other entities to make full use of this data, as described in this paper. Appended are: (1) Survey Participants; and (2) Virginia&apos;s Restricted Data Use Agreement. (Contains 6 footnotes.) [This document was produced with the National Center for Educational Achievement.]