Big Data and the University What lies ahead?

Vince Kellen, Ph.D. CIO, University of Kentucky

AIIM Executive Leadership Council

London, UK September 6, 2012

Higher education is undergoing a little soul searching right now…

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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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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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But we also over-react

OMG! Batten down the hatches!

Adjust course now!

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In June, 2012, UVa president Teresa Sullivan was fired after just 22 months for

not taking bold and quick action

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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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WE HAVE ONE MAGIC ANSWER FOR OUR TRAVAILS, ONE

FINAL HOPE FOR OUR DREAMS…

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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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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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Where are we going? What are we doing?

Architectural model

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SAP

PS

Banner

Bb Canvas

Open Class

D2L

Hobsons Sales Force Right

Now

EMAS

Moodle

Custom Apps

Clickers

ERP

LMS

CRM CUST

Basic Model

Industry reference

model

Institutional model

Institutional model

Institutional model

Source data Lift & shift operations

Conformance model

Basic model

Industry model

Derivative models

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Delivery tools

WORKFLOW

MOBILE ACCESS

VISUALIZATION

SAP workflow Bus Objects Access, Excel Tableau, etc.

SAP, Bb, open source,

etc.

Audience

Student

Friends

Family

Staff

Faculty

Architectural model

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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DATA IN ACTION EXAMPLE

Student perspective: self service

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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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To understand what is used to generate my K-Score

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How to improve the Blackboard portion of your K-Score

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How to improve the GPA portion of my K-Score

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Even the survey responses lead to overall improvement advice.

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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!

IT’S A WHITE-HOT SPACE RIGHT NOW…

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Everyone wants to distort the competitive field

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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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“Excuse me. I just wanted to ask a question. What does God need with a starship?”

- Captain Kirk

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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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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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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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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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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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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We have to change our action model

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Collect data

Build, change model

Validate model

Implement model

6 months – 5 years per cycle

Do

Learn

2 weeks – 3 months per cycle

Model B: Learn-Do fast Seek engagement Embrace failure

Model A: Build-Deploy slow Seek mastery Avoid failure

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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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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Let’s go surfing now, everyone’s learning how…

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Thank you!

Questions?

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