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)

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)

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

Some IEBC Project History

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

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

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

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

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

HI-PASS History

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

Overall Plan

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)

P20W SLDS System Overview

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

Data Linking and Expectations

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

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

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

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

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

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

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

Output / Reporting

Web-based Query Reports

Progress indicators within the LLDI Dashboard

Program Review

Intervention Program

Transition Across Segments

Reporting

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

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