Delivery of State-Provided Predictive Analytics to Schools: Wisconsin&apos;s DEWS and the Proposed EWIMS Dashboard. WCER Working Paper No. 2016-3
Author: Clune, Bill
Date: 2016-07-00
Type: Reports - Descriptive
Subjects: Predictive Measurement,Delivery Systems,State Standards,Early Intervention,Management Information Systems,Middle School Students,Models,State Policy,Organizational Change,Administrative Organization,College Readiness,Educational Indicators,Dropout Prevention

Since 2012, the Wisconsin Department of Public Instruction (DPI) has maintained a statewide predictive analytics system providing schools with an early warning in middle grades of students at risk for not completing high school. DPI is considering extending and enhancing this system, known as the Dropout Early Warning System (DEWS). The proposed enhancements include better understanding how and why schools use a tool like DEWS, supports and training necessary to translate DEWS into school change, and extending DEWS into other domains such as college and career readiness. This paper identifies national models of predictive analytic systems in education, including a focus on the Early Warning Implementation Monitoring System (EWIMS) (National High School Center, 2013). The paper explores how such policies might succeed in achieving their goals (e.g., dropout prevention and reduction of predictive at-risk behaviors), ways that districts and schools can make the policies more successful, and how states and state agencies like DPI might strengthen the policies, thereby facilitating local success. The paper recommends that DPI consider: (1) fostering a network of schools for professional development and support of implementation of predictive analytics like DEWS and EWIMS; (2) developing modifications of predictive analytic indicators to measure short-term change and progress; (3) merging predictive analytics with findings of current research funded by the statewide longitudinal data system grant that will identify effective strategies for supporting students with different at-risk profiles; (4) soliciting schools for voluntary implementation of the full DEW/EWIMS model; and (5) sponsoring research on existing practices of how schools identify and intervene on behalf of at-risk students. The analysis and recommendations of the paper should not be considered as final but rather as material for further discussion and deliberation--in essence as food for thought and inquiry. The paper is organized as follows. First is a description of the details of DEWS as an example of implementation of a predictive analytics tool. Second is a logic model of the policy, which is the theory of change underlying its intended positive effects on outcomes. Third, beginning an initial assessment of the theory of change tracing the policy from schools and students, is an analysis of the strength of predictive analytic policies, using a framework developed by Porter, Floden, Freeman, Schmidt, &amp; Schwille (1988). Fourth, following the logic model to the school level, is an analysis of the characteristics of organization and process required for successful implementation in schools, using a framework developed by Gamoran and colleagues (2003). Finally, the paper turns to the question of how outside agencies might enable successful implementation of predictive analytics, with a description of the results of a study of how three school districts supported use of college readiness indicators, followed by a discussion of how DPI might strengthen its own policies.