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. 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. Source: Tyton Partners, “Driving Towards a Degree, The Evolution of Planning and Advising in Higher Education, Part i,” 2016.
AChanging Landscape

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. Learning Analytics Value Propositions
Continue to Migrate and Evolve
• Completion
• Retention/
• Gainful employment
• Personalization
• Quality

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. Analytics Bring Order and Meaning to Data

8. Source: Johal, Navneet, “2015 ICT Enterprise Insights in the Higher Education Industry,” Ovum Research, 2015
The Promise ofAnalytics

9. Gartner Research Analytics Model, 2012

10. http://bit.ly/2o0t7Iy
Beyond Prescriptions: Machine Learning

11. http://bit.ly/2aoQwIx
But Wait, there’s More: Machine Learning to
Deep Learning andArtificial Intelligence

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. 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. 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. 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. 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. 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. Hae Okimoto, Ph.D.
Interim VP, Student Affairs
Director, Academic Technologies
University of Hawaii System: On
Becoming a Data-Empowered System

19. University of Hawaii System

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. 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. PAR Student Watch List
Honolulu Community College – Associate in Science
Selected Students

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. GPS

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. 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. 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. “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. “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. 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. Discussion and Questions

32. Thank you for joining us!