New book covering the missing elements critical to success in building data capacity in education agencies. Authored by 3 data analysts with expertise in public education agencies.
Delivery of State-provided Predictive Analytics to Schools: Wisconsin’s DEWS and the Proposed EWIMS Dashboard
Situates the Wisconsin Dropout Early Warning System in the context of national models of predictive analytic systems in education, including a focus on the Early Warning Implementation Monitoring System (EWIMS).
Article examines English Language Learner reclassification policies using a regression discontinuity framework to show that being reclassified as fully English proficient has positive effects for students' educational outcomes.
The award-winning Wisconsin Dropout Early Warning System was co-developed with educators to ensure that the sophisticated enterprise scale machine-learning provides schools with a timely list of students who should be considered for additional support and attention.
Describes the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades 6-9. Explains how DEWS' publicly available software modules can be applied to new data and outcomes.
Examines the degree of democratic control communities exercise over school boards through elections, combining election results for Wisconsin school districts with administrative records on local conditions.
This data simulation package creates realistic synthetic data that allows users to collaborate across agencies without privacy concerns or the need for a data sharing agreement, while writing code that can then be translated back to the original data.
Civilytics provides R training ranging from single workshop introductions to R to advanced R training on spatial data analysis, web application development with Shiny, predictive analytics and more. The 'model and coach' approach is central to many of Civilytics' trainings.