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.
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).