1. Margaret Heritage, CRESST Raymond Yeagley, NWEA

2. National Forum on Education Statistics  Mission: improve the quality, usefulness, timeliness, and comparability of education data  State/Federal/Local partnership with associate members  Primary method of service delivery is communicating shared ideas and best practices through meetings and publications  http://nces.ed.gov/forum

3. Why A National Data Model? Creating a more complete data picture of the pK-12 environment will help to:  Facilitate data transfer between applications and data storage within schools and districts  Conceptualize how to streamline vertical reporting from the school, to district, regional, state, and ultimately to the federal level.  Enable development of tools educators need to collect and analyze the data necessary to improve instruction and increase student learning.  Guide RFP writers/responders, vendors, teachers, administrators, researchers, and others in better organizing and using data

4. Initial Framework: Education Organizations SEA Board LEA Staff Facilities LEA Food Service School Transportation LEA Board Finance Accountability Intermediate Unit SEA

5. Initial Framework: Teaching and Learning Professional Development Qualifications StaffCourse/Class Student Instructional Programs and Curriculum Safety and Discipline Health AttendanceAssessment Participation & Activities Programs

6. Intent of the Task Force  Create an organized collection of  concepts and entities that need to be tracked  multidimensional structure that includes relationships  Define relationships among the entities as a first- order concept – i.e., relationships are as important as data elements.  Avoid replicating data dictionaries. Use standards and established definitions, linked to NCES handbooks and other resources.  Establish that a major feature of the model will be unique sets of attributes for each entity, and the relationships among entities.  Deliver the model through a system that will invite constant expansion and update.

7. Defining Relationships with OWL and XML:

8. What are the persons, places, things, or events we need to know about? What do we need to keep track of to answer the questions? What do we need to know? What are the important questions that need to be answered? Attribute Functional Relationship Taxonomy of Entities Organize Conceptual Model Assert Data Elements List Question/Issue The Process What are the important relationships among the entities that we need to include in the model? What are the measures, characteristics, or other information about the Entity that we need to collect and store? Entity Sample Attributes Phone Number IM Address Name Type of Instruction (e.g. Monitored, Mainstream) Assessment Score Type Learning Style Self-efficacy Sample Entities School Bus Student Teacher Assessment Score Lesson Skill Perception School Asking questions about Individual student and cohort growth over time Student characteristics related to performance Special program and instructional strategy effectiveness Return on investment Teacher characteristics related to student growth School performance characteristics Parents and community Impact of federal, state, and local policy on learning

9. Teaching and Learning Subgroup  What are the questions that need to be answered by states, districts and schools about teaching and learning?  What are the attributes that states, districts and schools will need in a database to answer the questions now, and in the future?  How can these attributes be measured?

10. Sample Questions  What instructional practices are related to what levels of achievement/motivation/self- regulation in core subjects/21st century skills for students with which characteristics?  What interventions with what characteristics are most closely associated with the greatest return on investment as measured by student academic growth per dollar spent?

11. Sample Questions  What type of assessment systems lead to increased use of assessment data and what are the effects on student achievement?  To what degree is the motivation of students with what characteristics related to curriculum relevance?

12. Attributes of the Program Entity Resources Used Identifier OutcomeLocus Measures of Effectiveness Characteristic Goals Program {Other Entity Relations} GUID Name Local ID State ID Academic Goals Non-academic Goals Participation Enrollment Targets Benchmarks Location Interface Schedule Staff Student Service Provider Person Outcome Non-person Outcome Program Availability NCES Program Type Program Periodicity Target/Served Population Characteristics Financial Human Curricular Facilities Equipment Technology

13. Sample Attributes: Instructional Programs and Curriculum  Clearly articulated learning goals  Curriculum coherence  Enacted curriculum  Interdisciplinary curricula  Curriculum relevance  Instructional strategies  Opportunity to learn  Learning environments

14. Sample Attributes: Assessment  Aligned assessment system  Evidence of learning:  Individual student achievement (e.g., summative state and district wide tests)  Artifacts (e.g., models, oral presentations, written work)  Observations  21st Century skills assessment (e.g., media and technology, team work, innovation and creativity)  Motivation  Self-efficacy  Rate of Learning

15. Consensogram  SEAs can have a significant impact on the quality and use of data by LEAs to increase individual student learning.  It is important for our SEA to provide direct support to LEAs related to use of student level data for instructional improvement.

16. Table Discussion  What is striking about the graphs on the wall?  How might these statements be addressed by different populations?  What is the best mechanism for gathering data related to the teaching/learning process?  What entities and attributes are essential for using data to increase student learning?

17. How can States Help?  Provide leadership  Establish expectations and policies  Create conditions  Model effective data use  Put systems in place  Provide training in data use (including measurement)

18. Table Discussion  Do you agree with what has been suggested about how states can help support LEAs?  if not why, not – how else do you think states could support LEAs?

19. Margaret Heritage: mheritag@ucla.edumheritag@ucla.edu Raymond Yeagley: Raymond.Yeagley@nwea.orgRaymond.Yeagley@nwea.org Vince Paredes: vparedes@sifinfo.orgvparedes@sifinfo.org