Establishment of a research collaborative connected to P20 WIN: At the conclusion of year 1 of the grant, we plan to establish a governance structure for research partnerships based on state policy priorities, expanding beyond education.
Execution of research projects through new collaborative: Once the new collaborative is established, we plan to execute 3 - 4 research projects, using SLDS data. The projects will be developed and completed in years 2 - 4 of the grant and will be designed to directly use SLDS data and to demonstrate the potential of the SLDS.
Upgrades to data sharing and matching process to facilitate research partnerships: In parallel to development of the new research collaborative, we will make changes to the P20 WIN data sharing process to enable effective research partnerships, and the production of high-quality data, securely, in a timely fashion. The upgrades will include a focus on professional development supports for the people that make P20 WIN work.
Add Data Sources. Connecticut proposes to add data sources to P20 WIN including social services, child welfare, higher education financial aid, and homelessness. These data will enhance the quality of information derived from P20 WIN studies and help improve education programs for the betterment of Connecticut’s most vulnerable citizens.
Build Analytic Capacity. This project will also expand P20 WIN’s analytic capacity across the participating agencies by hiring one staff member at each agency to form an improved Data Steward Committee responsible for data preparation and analysis.
Boost Data Matching Capacity. The project will secure data matching capability for its federated system by hiring a dedicated data matching analyst and implementing a more robust tech stack.
Produce Research and Corresponding Data Tools: With the added analytic capacity at the Participating Agencies and partnerships with researchers, Connecticut will conduct research into five primary topics and produce interactive data tools that will increase data access, and support data use for better policy that improves education for all students.
Student-schedule-staff module: track course taking patterns for monitoring and evaluation purposes, and to more efficiently determine high qualified teacher status for NCLB.
Establish a statewide course code taxonomy using NCES course code standards.
Develop a vertical SIF component to facilitate the data transfer process between the SEA and LEA's student information systems.
Create a student-schedule-staff module and data mart that contains student demographic and assessment results data, course type and associated teacher data such as age, years of experience, degree held, certification type, and teacher preparation program attended.
Enhancement of secure data dissemination for SEA and LEA decision support.
Establish a statewide course code taxonomy using NCES course code standards and develop intermediary application for course mapping.
Develop a vertical SIF component to promote interoperability between the SEA and LEA: partnership with districts using the Powerschool® SIS.
Create a Functional Requirements Document (FRD) for the Zone Integration Server (ZIS) installation and configuration, as well as the Powerschool® agent creation and/or integration.
Specify a data model for course-taking and completion information.
Work with pilot districts to install and configure a ZIS within their application hosting environment.
Creation of student-schedule-staff module and data mart to store course taking and staff data.
Data Mart Schema.
Data Mart Specification.
The Deployment of an Enterprise-wide Persistent Data Facility.
The Deployment of an On-going Operation and Maintenance Strategy.
Enhancement of secure and public dissemination of student-schedule-staff data mart.
The specification and development of an enterprise-wide Persistent Data Storage Facility.
The development of data marts, facing applications and decision support cubes used to disseminate data from the persistent data stores to the district, school, student, parent and public level.
Provide training to LEA staff on the appropriate use of persistent data, decision support tools, identity management and password provisioning.
A collaboration with the University of Connecticut Health Center, to develop a longitudinal research data warehouse, federating de-identified data from the Connecticut Departments of: Education; Children and Families; Public Health; and Mental Retardation.
Develop SIF Zones and SIF agents to pilot horizontal and vertical reporting models(possible collaboration with other states).
Develop a data dictionary to serve as a conclusive meta-library for elements collected on behalf of the department (possible collaboration with other states).