1. 10:15am, February 14, 2013 2013 NCES MIS CONFERENCE
TITLE: Data Linking for Analytics—K–12 to Community College to University
10:15am, February 14, 2013
Watson (IEBC), Osumi (UH), Ikenaga (UH)

2. Introductions Jean Osumi Todd Ikenaga John Watson
Senior Associate for Academic Policy and Evaluation Hawaii P-20 Partnerships for Education, University of Hawaii
Todd Ikenaga
SLDS Program Manager Hawaii P-20 Partnerships for Education, University of Hawaii
John Watson
Director of Analytics, Institute for Evidence Based Change (California)

3. Agenda Background, Systems, Approach
HI-PASS History, Current Effort, Data Sources
Cross-Segment Data Linking
Reporting
Progress, Next Steps

4. Some IEBC Project History

5. Our first multi-segment project: Cal-PASS
Started in San Diego in 1998
Became a State funded project in 2003
Goals:
Collect actionable data
Links primary, secondary and post-secondary institutions on a regional basis
Tracks students from one segment to the next

6. Using Data From A Systems Perspective
Educational System
Technology
and Research Expertise
Organizational Habits
Human Judgments and Behavior

7. Keys to Data Use Focus on a few key metrics
Key Performance Indicators, a good way to start
Focus on the goal – student learning and completion
Track cohorts, not just snapshots
Look at leakage points
Use data as a way to improve, not to punish
Most important – tell a story

8. Systems Main Warehouse Research DB Validation > Loading ETL >
Web Site/Portal
Reporting
In-SITES Tools
(Development | Published)
ETL >
Data mart > Data Store

9. Data vs. Use
We got it wrong – need to focus on consumer; less on the data
Ron Thomas rules for Data Analysis…
We are in the knowledge Business – not the data business
Data is about improvement, in particular improvement in instruction
A protocol for using data is important
Must build capacity of practitioners to acquire and use data

10. HI-PASS History

11. HI-PASS Initial Groundwork
Started with two groups with different purposes: Maui (PLC) & Hawaii P-20 (assess statewide impact)
2009 Statewide Forum on Longitudinal Data
top priorities that emerged were data governance and access to data, which drove overarching MOU
One-by-one data sources and funding became clear – all focusing on P-20 as an end-goal

12. Overall Plan

13. College and Career Readiness Indicators
Completed for every public high school
Classes of 2008, 2009, 2010, 2011
Measures by school
College access nationwide
SAT scores
Percentage of completion of the BOE Recognition Diploma
College level work: Advanced Placement and Running Start
College level and remedial/development enrollment for Math
and English (UH only)

14. P20W SLDS System Overview

15. Challenges MOUs Lawyers Changing culture Data Quality Sustainability
Federal and State regulations

16. Data Linking and Expectations

17. Technology Microsoft SQL Server, Enterprise Edition Dundas Dashboard
Windows-compliant CMS

18. Multi-segment linking
CA: Multiple IDs across segments
K-12 > CC
Encrypted provided derived key
Derived Key
K-12 > University
CC > University

19. Multi-segment linking
Gulf Coast-PASS
Texas
Texas Pathways (Higher Ed Coordinating Board)
Encrypted identifier
GC-PASS
Provided encrypted identifier > derived key

20. Deduplication Techniques
There are times when we suspect duplicate records, or specific keys aren’t available.
Especially found in cross-segment situations
Some instances:
1) name change
2) typos in name or birthdate
3) detecting false matches
4) detecting CONFLICTING IDs
5) finding transposed names across institutions

21. Deduplication Techniques
Remedy: multiple-pass deduplication, including:
Creation of metakey based on various techniques
These agree with primary derived keys 95% of time
Current Method (23 stages):
Rule-based cleaning
Comparison vectors
Cosine similarity

22. Labor Data: Promises, Pitfalls
For an increasing number of projects, there is the hope of understanding what happens to student cohorts, population as they leave school
Labor data can be the answer
Additional MOUs
Data formats can vary
Data security concerns lead to carefully-planned processing
Results promising. Example: Coachella Valley, CA

23. HI Data Linking
Demographics matching across K-12 to postsecondary mainly using name, date of birth and gender in different combinations with strictest criteria used first.
Match 1 - last name, first name, dob, gender
Match 2 - last name, first name, dob
Match 3 - last name, first name (imbedded), dob, gender
Match 4 - last name (imbedded), first name, dob, gender
Match 5 - last name (imbedded), first name (imbedded), dob, gender
Match 6 - last name, first name (first 3), dob, gender
Match 7 - last name, first name (first 3), dob (month/year), gender
Match 8- last name (first 3), first name (first 3), dob, gender
Match 9 - last name (first 3), first name (first 3), dob (month/year), gender
Match 10 - last name, first 3 letters of FN, dob (month/day), gender

24. Output / Reporting

25. Web-based Query Reports

26. Progress indicators within the LLDI
Dashboard

27. Program Review

28. Intervention Program

29. Transition Across Segments

30. Reporting

31. HI-PASS: Focus of output
OLAP Cubes
K12 to postsecondary transitions - focus on remedial enrollments and placement
Postsecondary enrollments linked to workforce data - Shows students, enrollments, GPA, campus, time period, linked to employment data
CTE tool -Similar to K12 but focusing on CTE pathways and subsequent majors and awards at postsecondary

32. Project Status Continue with HI-PASS to identify issues
Data quality
Integration problems
Missing elements
Definitions and standards
Complete infrastructure build for permanent system
Continue working on our data governance framework
Research
Security and Access to data
Establish a framework for data literacy and use
Focus on identifying, training diverse user levels