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News & Updates

"The NEDM project has been very helpful in the ongoing expansion of the eScholar Complete Data Warehouse. By leveraging the work of the NEDM in finding commonality among education data attributes across disparate systems and organizations, we are able to more quickly deliver broadly applicable enhancements."
-Shawn Bay
 Founder/CEO, eScholar LLC

“What I’ve found helpful about working with NEDM is that it provides us with a starting point for our work to expand our data system. In New Jersey, we have multiple, fragmented data collections that grew over the years as independent entities. Our challenge is to build out our SLDS to incorporate what we’ve been collecting but also focus on what we may not have been collecting. NEDM is a fabulous tool to help us establish our priorities for which data elements and collections should be incorporated first and also to provide the more global guidance of what the data values and formats should be for each new data element.”
-Bari Anhalt Erlichson PhD
 Director Office of Research and Evaluation, New Jersey Department of Education

About the Data Model

This is the first non-proprietary national education data model to help schools, LEAs, and states design or guide the selection of systems for instructional delivery, data driven decision making, data collection, operations, and reporting.

The Education Data Model is a comprehensive localized education data model that provides a national blueprint for schools. This blueprint enables schools to evaluate and improve instructional tools, communicate those needs to their umbrella agency or directly to vendors, enhance the movement of student information from one LEA to another, and in the end, have better tools to inform instruction.

The Education Data Model helps to answer questions such as:

  • What data do schools, LEAs, and states need to collect and manage at the local level to meet the information needs of students, staff, and other stakeholders?
  • What data are needed to effectively manage education organizations to improve the success of teaching, learning, and school leadership?
  • What data is needed to efficiently manage and run an education organization from a fiscal and administrative perspective?

Data modeling is a family of techniques to describe the types of information important to an enterprise. Most would agree that the enterprise of education is in need of continuous and expansive conversations around information necessary to facilitate everything from the teaching and learning process to making federal policy decisions.

Overall, data modeling is a critical tool, without which enterprises have little hope of thriving or complying with government regulations and industry standards. Data modeling is no longer limited to databases, no longer just the tool for IT staff, no longer focusing exclusively on technology, and no longer optional.

The Education Data Model details a conceptual representation of the education information domain focused at the student, instructor and course/class levels. It delineates the relationships and interdependencies between the data elements necessary to document, operate, track, evaluate, and improve key aspects of an education system. The Model aspires to be a comprehensive, non-proprietary inventory and a map of education information that can be used by schools, LEAs, states, vendors, and researchers to identify the information required for teaching, learning, administrative systems, and evaluation of education programs and approaches. The Data Model will continue to develop and expand in a manner open to all education stakeholders.

The Model

The Model:
  • Is not a data dictionary but a data inventory
  • Is linked to the NCES Handbooks Online
  • Allows for the use of definitions from other sources
  • Is comprehensive, dynamic & ongoing
  • Will have formalized update cycles

Development of the Data Model

The development of the Data Model involved taking important education questions, issues, or processes, and identifying the things (entities) that need to be tracked in order to answer the question, address the issues, or reflect the processes involved.

Next, for each thing identified, appropriate measures or descriptions (attributes) of the things are identified that are required in order to answer the question, address the issue, or reflect the process.

Finally, the important logical relationships among the things (relations) are identified. These relations reflect the real-world function of each thing (entity) and add more meaning to the model.


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