This presentation discusses the current issues in higher education and how big data plays a big role.

1. Big Data and the University
What lies ahead?
AIIM Executive
Leadership Council
London, UK
September 6, 2012
Vince Kellen, Ph.D.
CIO, University of Kentucky

2. Higher
education is
undergoing a
little soul
searching right
now…
2

3. n Our tuition costs are rising too fast
• Starve the beast and it will reform!
n We don’t teach the things industry needs
• But our graduates may have to switch jobs/careers!
n High-priced administrators are ruining higher ed
• Faculty should have more power!
n The tenure system is ruining higher ed
• And we want tenured faculty to run the place?
n Education will be free and the university will perish
• And who will educate my nephew?
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4. Rather than accept the need
for deep change, in academia
we have perfected the highest
form of denial
We use big words, arcane
terms. We muddy the waters
to make them look deep. We
let our use of language exceed
our use of logic
We do this better than
ANYBODY
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5. But we also over-react
OMG!
Batten down
the hatches!
Adjust course
now!
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6. In June, 2012, UVa president Teresa
Sullivan was fired after just 22 months for
not taking bold and quick action
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7. 7

8. A couple weeks later and after support
from faculty, staff, students and governor
‘prodding,’ the UVa board unanimously
reinstated Teresa Sullivan
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9. 9

10. WE HAVE ONE MAGIC ANSWER
FOR OUR TRAVAILS, ONE
FINAL HOPE FOR OUR
DREAMS…
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11. 11

12. Big data topics
n Insights into students
• Improve learning through personalized instruction
• Keep students motivated, engaged, on track (retention)
• Who they are, what they do, how they think
n Insights into logistics
• What blocks student progress? Degrees? Courses? Aid?
• How efficiently are our facilities, faculty used?
• Revenue and cost data per region of space per business
line (research, education, resort/entertainment, healthcare)
n Transform the enterprise
• It’s a both/and world. Combine efficiency with quality gains
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13. Typical sources of data
n Student information systems
• Demographics, financial information, incoming test scores,
transcripts, schools attended, course history, history of adds/drops,
learning management system click stream, student groups
enrollment, attendance at events, student alerts data, use of tutors,
course capture viewing, degree progress runs, emails
sent/responded to, dining information, social network, IT support
calls, security swipes, survey responses, etc.
n ERP systems
• Financial, facilities, procurement, HR, etc.
n External data
• National clearinghouse data, state longitudinal data, research data,
lists of prospects
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14. Where are we going? What are we doing?

15. Architectural model
Lift & shift Conformance Basic Industry
Source data Derivative models
operations model model model
PS E
SAP R
Banner
P
Institutional model
Canvas
Open
L
Bb
Class M Industry
D2L S reference
Moodle model
Basic Institutional model
Model
C
EMAS Hobsons
Sales
R
Force
Right M
Now
Institutional model
C
Custom
Apps
U
S
Clickers
T 15

16. Architectural model
Delivery tools Audience
Student
V M
I O
S B
W
U I
O
A L
R
L E
K Friends
I
F
Z A
L
A C
O Faculty
T C
W
I E
O S
N S Family
Staff
SAP workflow Bus Objects SAP, Bb,
Access, Excel open source,
Tableau, etc. etc.
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17. Embed analytics in many activities: target use cases
n Actionable information. Replicate data, build models and deliver via BI tools
1. Scoring of predicted student graduation likelihood
2. Analysis of retention by segments with drill-down to detailed student data
3. Ad-hoc analysis of ongoing retention questions
4. Social media ingestion to find students who need help, areas of concern
n Information in action. Trigger intelligent workflows to spur student interactions with
the institution, each other
1. Highly automated, overlapping micro-segment management
2. Automated prediction and escalation of student alerts, recommendations when the
system detects concerns
3. Real-time analytics to personalize on-the-fly adaptive learning objects
4. Student self-service recommendation tools (recommend a study-buddy, evaluate my
social network & give me tips, review my predicted graduation score, recommend
advising sessions to me). Give students real-time performance feedback, and a target
5. Target and personalize the earning of points for students at specific recommended
engagement areas (timeliness on assignments, grades, advising sessions, student
clubs)
6. Have students opt parents and friends into the notification system
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18. DATA IN ACTION EXAMPLE
Student perspective: self service
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19. A K-Score is a prediction of success.
It’s used to give students an
understanding of how well they are
doing over time.
We use factors such as their
academic work, how engaged they
are in Blackboard and engagement
in campus activities to generate a K-
Score.
Over time, we’ll add more factors to
improve the accuracy of this score.
We also rely on traditional, non-
evasive survey techniques to help
round out the student performance
statistic.
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20. To
understand
what is used
to generate
my
K-Score
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21. How to improve
the Blackboard
portion of your
K-Score
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22. How to improve
the GPA portion
of my K-Score
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23. Even the survey
responses lead
to overall
improvement
advice.
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24. Future versions of the student
self service apps will include:
• Reminder Services
• Planning &
Recommendation Services
• Advisor Communication and
Appointments
• Continual, quarterly
improvements
• And more! Stay tuned!
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25. IT’S A WHITE-HOT SPACE
RIGHT NOW…
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26. Everyone wants to distort the competitive field
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27. Will the Stanford AI course change
everything?
Will VC Edutech take off? Will
Harvard/MIT EdX rule?
Will online replace face to face?
Will badges replace degrees?
Will top faculty become itinerant
millionaire e-faculty?
What will employers really value?
Will any of this big data stuff work?
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28. “Excuse me. I just
wanted to ask a
question. What
does God need
with a starship?”
- Captain Kirk
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29. In times of chaos, return to strategy fundamentals
n Will the new thing help solve a critical problem? And for
whom? How many? Exactly how?
n How valuable is the thing in question? What is it worth?
• Badges, free course, data about the learner, learner
eyeballs, transferred credit (Colorado State & Udacity)
n Can the provider/seller gain access to a resource of some
kind that no-one else can get?
n What parts of the new thing easily replicable? What parts
aren’t?
n What barriers keep new competitors out?
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30. The value of big data in higher education
n Let’s set other big data research aside
• E.g., ‘dark matter’ in DNA, 15 peta bytes, 300 years of computer time
n Deep personalization of messaging and learning content is big
• Billions across the globe who need more than what we offer now
• Ability to automate many (not all) aspects of teaching and learning
• We can help improve student engagement, graduation
• We can promote better learning
• We can provide lower-cost lifelong learning
n Imagine
• If higher education had invested $$ into personalizing online education
as much as Google, Microsoft, Yahoo, Facebook and others have
• Where would we be today? What would we be today?
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31. What is deep personalization?
n Social
• We naturally adjust what we communicate in social settings. Face-to-face
communication lets us interpret cues consciously and non-consciously
• Digital social interactions are nice, but…
• When digital interactions let us suspend disbelief, they will have parity with
molecular interactions
• Something as difficult and complex as transformational education usually
requires HIGH socialization (Abraham Lincoln aside)
n Individual
• Visual and verbal concepts, terms, text, tone and style can be altered based
on individual differences in
– Cognition (working memory, visual/verbal, reasoning, reflection…)
– Affect/personality (need for sensation/cognition, optimism, confidence,
effort, self efficacy, identity, persistence…)
• We do this automatically in F2F interactions. How can the computer do this?
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32. Big data and competition
n What is scarce, difficult or doesn’t scale well?
• Data integration, large network effects, brand equity, some content
• Exceptional faculty, top executive-managerial talent
n What is idiosyncratic to the institution?
• How the student actually ‘flows’ through a specific university. E.g., campus
culture, student life, facilities, student peer interactions
• Tenured faculty
• Decision processes, geography
n What new or dynamic capabilities does this create?
• Rapid insight to data may mean quicker/better allocation of resources, better
market share growth, more accurate and speedier decision processes overall,
smarter students, new services created faster/better/cheaper (FBC)
n What is easily replicated?
• The core technology, a sizeable body of content, business processes
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33. How a caterpillar turns into a butterfly
n A caterpillar carries genetic material called “imaginal buds” on its
underside. It eats a lot and gets fat
n Hormonal changes cause the caterpillar to build a cocoon and go
dormant. The imaginal buds ‘awaken’
n These buds begin to join together and slowly become the butterfly
by digesting the plump body of the caterpillar
n In essence, the caterpillar carries, unknowingly, something that will
kill it, eat it and become the butterfly
n Tell that to your 6-year-old!
Who is the caterpillar? Who are the imaginal buds? What the
heck is getting hatched?
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34. New core competencies and data
n Higher education is being forced to develop two new core competencies,
previously thought incompatible:
• Cost effectiveness
• Superior knowledge of the customer
n At the center of both of these competencies lies data and analytics
• We are awash in all sorts of data
• Universal data impedance theorem: those who could use it, don’t have
it. Those who have it, don’t use it
• Not all of this (if any) is big, but all of it is fast
n The VC edutech market is looking like a fight over data
• Data analytics to deliver relevant content to learners
• Data assets to be used later to develop a viable revenue model
• Unsurprisingly, elite institutions moved first on MOOCs. Do they have
more to lose?
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35. We have to change our action model
Build,
Collect Validate Implement
change
data model model
model
Model A:
6 months – 5 years per cycle
Build-Deploy slow
Seek mastery
Avoid failure
Model B:
Learn-Do fast
Seek engagement Do
Embrace failure
Learn
2 weeks – 3 months per cycle
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36. We have to change people
n Staff
• Business process and efficiency excellence
• Acumen, knowledge, skills
n Leaders (Deans, VPs, etc.)
• Business process and efficiency excellence
• Collaboration, people-savvy, culture changing, mountain-moving
n Faculty
• Teamwork, people-savvy, shift away from bi-polar thinking
• Continue to build quality interaction with and accountability to society
regarding teaching, understanding of modern efficiency concepts
n Boards
• Deeper conceptual understanding of the academy
• Better knowledge of HE industry competitive dynamics
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37. We have to expand our thinking
n Crowd sourced analytics
• Within the company
• Across the globe?
n Super-fast, real-easy data movement
• In-memory analytics may change things
n Imagination
• We have to prime the pump of ideas
• Where you start does not matter if the iteration speed is high and the dialog
across boundaries is good
• Hover over counter-intuitiveness, things that bother you
• Try to see what you aren’t seeing
n Security
• New forms of protection, anonymity
• Third parties to provide security services?
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38. Let’s go surfing now, everyone’s learning how…
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39. Thank you!
Questions?
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