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Common Data Definitions

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Presented by Dr. Ellen Wagner, Hobsons, and Dr. Hae Okimoto, University of Hawaii at the ASU+GSV Summit 2017.

Published in: Education
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Common Data Definitions

  1. 1. Student Success forAll: Common Data Definiti at Work Ellen D. Wagner, Ph.D VP Research, Hobsons Hae Okimoto, Ph.D. Dir Academic Technologies University of Hawaii
  2. 2. Our nation’s focus on student success has generated multiple ways of measuring progress and completion. The commonly defined, openly shared data definitions developed by the PAR Framework give Hobsons’s customers the opportunities for conducting research comparative evaluation and sharing best practices that are valid, reliable and generalizable for ALL students and the people working to ensure their success. Student Success forAll: Common Data Definitions at Work
  3. 3. Source: Tyton Partners, “Driving Towards a Degree, The Evolution of Planning and Advising in Higher Education, Part i,” 2016. AChanging Landscape
  4. 4. Data Have Changed Everything • Analytics have ramped up everyone’s expectations of personalization, accountability and transparency. • Academic enterprises cannot live outside the institutional focus on tangible, measureable results driving IT, finance, recruitment, content and other mission critical concerns
  5. 5. Learning Analytics Value Propositions Continue to Migrate and Evolve • Completion • Retention/ • Gainful employment • Personalization • Quality
  6. 6. “The difference between what we’re collecting and what we’re reporting on is huge.” - Source: Yanosky, Ronald, with Pam Arroway. “The Analytics Landscape in Higher Education,” 2015. Louisville, CO: EDUCAUSE Center for Analysis and Research Data is Collected, Not Connected
  7. 7. Analytics Bring Order and Meaning to Data
  8. 8. Source: Johal, Navneet, “2015 ICT Enterprise Insights in the Higher Education Industry,” Ovum Research, 2015 The Promise ofAnalytics
  9. 9. Gartner Research Analytics Model, 2012
  10. 10. http://bit.ly/2o0t7Iy Beyond Prescriptions: Machine Learning
  11. 11. http://bit.ly/2aoQwIx But Wait, there’s More: Machine Learning to Deep Learning andArtificial Intelligence
  12. 12. From Research to Practice The Evolution of PAR Framework 2011 The PAR Framework was founded as a Gates Foundation project within WICHE as part of WCET. PAR openly licenses and publishes data definitions and Student Success Matrix 2012 PAR became fully independent, not-for- profit software as a service membership collaborative. 2015 Hobsons acquired the assets of PAR as part of its portfolio of Student Success solutions (which includes Starfish). 20162013 PAR developed the Student Success Matrix as a common way to classify interventions.
  13. 13. PAR Framework Research Questions • Can predictive analytics find students at risk with the data we have in hand? • Can risk differences between and among student sub-populations in an institution be discerned? • Will students from anomalous institutions be discernable?
  14. 14. PAR Framework Common Data Definitions Student Demographics • Gender • Race • Prior credits • Permanent resident zip code • High school information • Transfer GPA • Student Type Course CatalogCourse Information Student Academic ProgressStudent FinancialsLookup Tables • Course location • Subject • Course number • Section • Start date / End date • Initial grade / Final grade • Delivery mode • Instructor status • Course credit • Subject • Course number • Subject (long) • Course title • Course description • Credit range • Credential types offered • Course enrollment periods • Student types • Instructor status • Delivery modes • Grade codes • Institution characteristics • FAFSA on file • FAFSA file date • Pell received / awarded • Pell date • Current major / CIP • Earned credential / CIP
  15. 15. Pioneered early alert and case management First to integrate multi-source data into common view Added our 200th higher education institution Joined Hobsons in 2015 Funded by Bill and Melinda Gates Foundation Led development of analytics-as-a- service First open source inventory of interventions Joined Hobsons in 2016 ADecade of Student Success, UNIFIED
  16. 16. Priorities • First-year student success • Adult and post- traditional learners • Programs to support underrepresented students • Transfer students (up, down, lateral) and pathways PAR Research Includes • ”An Empirical Look at Intervention Effectiveness for Improving First Year Experiences,” Presentation by PAR’s Ellen Wagner, PhD., Oct 2015 • “Expansion for Evaluation of CAPL 101/Jumpstart – UMUC Student Success,” Report by PAR’s Ellen Wagner, Ph.D., Scott James, and Cassandra Daston. June 2015 • “Retention, Progression, and the taking of Online Courses,” Online Learning peer-reviewed study by Dr. Karen Swan (UIS) and PAR’s Scott James and Cassandra Daston, June 2016 • “Predicting Transfer Student Success,” whitepaper by Scott James, PAR Data Scientist. May 2015 • https://www.hobsons.com/resources/entry/improving-post-traditional- student-success Mission Alignment
  17. 17. DataAwareness Has Highlighted Misalignments in the U.S. Education System • Points of transition typically represent points of loss in the system. • What can we do to optimize digitalization to increase student success, improve institutional effectiveness and efficiency and reduce cost?
  18. 18. Hae Okimoto, Ph.D. Interim VP, Student Affairs Director, Academic Technologies University of Hawaii System: On Becoming a Data-Empowered System
  19. 19. University of Hawaii System
  20. 20. “55% of Hawai‘i’s working age adults to have a 2- or 4- year college degree by the year 2025.” 43% 42% 44% 0% 55% 2007 2011 2015 2019 2022 2025 %ofPopulationw/ Degree Current Trend GOAL Cumulative Degree Gap: 42,932 degree holders Source: UH Institutional Research and Analysis Office, NCHEMS, & U.S. Census Bureau, American Community Survey, 1-year estimates, 2006 to 2012 }
  21. 21. HGIStrategic Direction Measures-2016 Degrees & Certificates Earned Grad Rates 4-YR Grad & Success Rates 6-Yr or 150% CC Enrollment to Degree Gap – NH Enrollment to Degree Gap – Pell STEM Degrees & Certificates Awarded UH Mānoa UH Hilo UH West O‘ahu Hawai‘i CC Honolulu CC Kapi‘olani CC Kaua‘i CC Leeward CC Maui College Windward CC Met or Exceeded Goal Within 0.3% of Goal for “Enrollment to Degree Gap” measure. Met or exceeded baseline for other measures.Did Not Meet Goal http://blog.hawaii.edu/hawaiigradinitiative/strategic-priorities/
  22. 22. PAR Student Watch List Honolulu Community College – Associate in Science Selected Students
  23. 23. Home Campus: Honolulu Community College Program: HON-Natural Sciences Pre-Major: Pre-Medicine PAR Level 1 PAR Factors: #1 Associates student, #2 Enter with no prior credits, #3 Low cred... 1 COMPASS Reading: 27 Semester Entered: Fall 2014 Registered for: 13 Credits at any UH institution Spring 2016 Registered for: 12 Credits at any UH institution Fall 2015 High School: Central High School 5/2014 Registered for: 15 Credits at any UH institution Fall 2016 Applications: 201510 Applicant Accepted at Honolulu, 201510 Accepted at Leeward ORG_MEMBERSHIP: HON-ALL-STUDENTS-FA2015, HON-FINANCIALAID-FA2015… COMPASS Math: 28 Career Interest: Health Science (medicine, dentistry, pharmacy, nursing, physical t…. Immediate Ed Goal: Take courses to transfer to another college Highest Ed Goal: Earn a Medical Degree Highest Ed Goal Institution: University of Hawaii Manoa
  24. 24. GPS
  25. 25. 60% 64% 35% 0% 50% 100% Corequisite Remediation StudentsCompletingwith%Corbetter ENG 22 + ENG 100 ENG 100/100S ENG 19 +ENG 22 + ENG 100 ENG 100/100T 56% 27% 82% 27% 0% 50% 100% MATH 22 + MATH 82 (Consecutive Semesters) MATH 82 (4 credits) MATH 75 (1 Semester) 75% 29% 70% 29% 0% 50% 100% MATH 82 + MATH 100* (Earned “C” or better 2nd Semester) MATH 103/88 (1 Semester) MATH 100/78 (1 Semester) CollegeMathTrackCollegeAlgebraTrack MATH 22 + MATH 82 (Consecutive Semesters) MATH 82 + MATH 103 (Earned “C” or better 2nd Semester) * Transfer level courses MATH 100 / 111 / 115 2+ levels below transfer level 1 level below transfer level Honolulu Community College Leeward Community College 25%
  26. 26. 21% 28% 17% 25% 34% 19% 28% 37% 22% 31% 38% 23% 0% 10% 20% 30% 40% 50% Total ≥15 Credits <15 Credits 2009 2010 2011 2012 UHMānoa4-YearGraduationRatesof First-TimeFreshmenCohorts,2009-12 GraduationYears2013-16 The Right 1515 to Finish
  27. 27. Action is Imperative Evidence is Essential Connections are Critical Time is Valuable Our Four Principles Good data can challenge and validate your assumptions, and catalyse innovation. Knowledge is only the beginning. You need to turn data into action to help all students. To support students effectively at scale, you need to work together, across functional groups. Your students need your best help now. You must act both quickly and strategically. “This work has allowed us to eliminate the duplication of services by multiple departments and streamline our programming to offer first class interventions to our student population. Michelle Wiley, Student Support Specialist, Penn State World Campus Evidence is Essential
  28. 28. “In order to achieve the goals in our strategic plan, it’s absolutely essential that we approach student success in a holistic way, with good data to drive decisions. Mark Askren, CIO, University of Nebraska - Lincoln Evidence is Essential
  29. 29. “We can’t just throw data at faculty and expect them to embrace it – and understand it – unless they realize that there’s a problem they’re trying to solve.” Larry Dugan, Director of Instructional Technologies, Monroe Community College (SUNY) Connections are Critical Source: Jankowski, Natasha A, “Unpacking Relationships: Instruction and Student Outcomes.” American Council on Education, 2017
  30. 30. Action is Imperative “I believe that as an institution of higher education, we have a moral obligation to offer all that is possible to assist with a student’s success. “ Dr. Francis L. Battisti, Executive Vice President and Chief Academic Officer, SUNY Broome Community College
  31. 31. Discussion and Questions
  32. 32. Thank you for joining us!

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