Matrix Systems & Technologies

Background:

The MLDS is a project that will design, develop, implement, and support the execution of a longitudinal data system that links student level data across prekindergarten through grade 20 (P20) and incorporates the linking of teachers to student level data from the data warehouse upon completion of cleansing transforming and ensuring data quality. The Maryland Longitudinal Data System Project (MLDS) represents an enterprise solution to the Data Quality Campaign 10 Essentials Elements of a Longitudinal Data System.

Services: 

• Project management and planning activities for the MLDS project.
• Creation of various project plans to deliver project objectives.
• Management of project team activities.
• Planning, organizing, and controlling resources to meet project deliverables.
• Management of the entire project process to ensure successful attainment of goals.
• Re-engineering of the Education Data Warehouse to accommodate new data elements.
• Addition of modules to the Web Data Collection System to facilitate data collection and reporting.
• Provision of advanced business analysis and reporting tools to an expanded user base.
• Planning and implementation of the collection of student-level course-related information for virtual student records.
• Increasing the capacity of the Maryland Unique Student Identification System (USIS) to include Higher Education and state Workforce data.
• Expansion of user access to MLDS data.
• Linking student data across multiple data collections and years.
• Reduction of silo structures of major program data collections and integration of the Special Education program with the state’s system.
• Increasing capacity to provide educators and policymakers with new and useful data reports.
• Opening access to data repositories for schools.
• Development of a standardized process with local school systems to define courses at elementary, middle, and high school levels, implementing data collection for MLDS.
• Conversion of all major data collections to a web data collection format with a Statistical Process Control feature for data accuracy.