Wisconsin’s state education agency, the Department of Public Instruction (DPI), is submitting an application to continue development of our longitudinal data system (LDS) in order to meet the demands of our state’s educational priorities: closing the achievement gap, building 21st century skills, and moving toward a PK-16 model in which all stakeholders—educators, students, business, government—can make informed decisions based on sound longitudinal data.
Developing a Longitudinal Data System to Support 21st Century Learning in Wisconsin is a proposal that will enable Wisconsin’s data systems to move in step with State Superintendent Burmaster’s vision of 21st century learning outlined in the New Wisconsin Promise. Our commitment is to close the achievement gap, equip students with 21st century learning, and create stronger links between PK-12 and the world of post-secondary education and/or work. So a rich educational picture of Wisconsin students can emerge in this data system—and we can evaluate whether Wisconsin is meeting the New Wisconsin Promise—we need to:
1) Build student-level datasets to inform a PK-16 data system, and to increase alignment to LDS data quality standards.
2) Move the public reporting of aggregate data into the LDS where disaggregated student data already resides to reduce redundant data and total costs
3) Develop (restricted-access) analytical tools and public reporting tools that allow a variety of users to access LDS data on an ad hoc basis.
In order for educators and other stakeholders to gauge progress on meeting these priorities, we need to have data that follows students over time, ideally PK-16 data. Currently, Wisconsin’s LDS system stores student-level achievement data from a variety of sources and while this data is critical, it is not comprehensive enough for our state’s demands and cannot answer pressing policy questions. For example, currently we do not have a way to analyze test scores alongside, or in comparison to, courses taken. We are limited by the few K-12 data contained in the LDS, and limited by the lack of a PK-16 data structure. By adding the elements listed above, we will be able to follow student achievement in a more holistic way, provide a more robust picture of student learning in Wisconsin, and a richer context for policy decisions. All of this is necessary if we are to move forward with our vision of preparing students for the 21st century and fulfilling the New Wisconsin Promise.
Expected Outcomes: Several outcomes are expected. We expect that adding three student-level datasets will lead to more robust research and analysis that will directly inform a PK-16 system. We expect that consolidating disaggregated student data with aggregated (publicly reported) data will result in improved data quality, and more accurate reporting. We expect a variety of stakeholders will begin to access the LDS for data mining by way of the interactive reporting tools we build, relying on the system as the central repository of education data in the state.
