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Big Data And The University

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

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Big Data And The University

  1. 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. 2. Highereducation isundergoing alittle soulsearching rightnow… 2
  3. 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? 3
  4. 4. Rather than accept the needfor deep change, in academiawe have perfected the highestform of denialWe use big words, arcaneterms. We muddy the watersto make them look deep. Welet our use of language exceedour use of logicWe do this better thanANYBODY 4
  5. 5. But we also over-react OMG! Batten down the hatches! Adjust course now! 5
  6. 6. In June, 2012, UVa president TeresaSullivan was fired after just 22 months for not taking bold and quick action 6
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  8. 8. A couple weeks later and after supportfrom faculty, staff, students and governor ‘prodding,’ the UVa board unanimously reinstated Teresa Sullivan 8
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  12. 12. Big data topicsn Insights into students • Improve learning through personalized instruction • Keep students motivated, engaged, on track (retention) • Who they are, what they do, how they thinkn 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 12
  13. 13. Typical sources of datan 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 13
  14. 14. Where are we going? What are we doing?
  15. 15. Architectural model Lift & shift Conformance Basic Industry Source data Derivative models operations model model model PS E SAP R Banner P Institutional modelCanvas 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. 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 StaffSAP workflow Bus Objects SAP, Bb, Access, Excel open source, Tableau, etc. etc. 16
  17. 17. Embed analytics in many activities: target use casesn 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 concernn 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 17
  18. 18. DATA IN ACTION EXAMPLEStudent perspective: self service 18
  19. 19. A K-Score is a prediction of success.It’s used to give students anunderstanding of how well they aredoing over time.We use factors such as theiracademic work, how engaged theyare in Blackboard and engagementin campus activities to generate a K-Score.Over time, we’ll add more factors toimprove the accuracy of this score.We also rely on traditional, non-evasive survey techniques to helpround out the student performancestatistic. 19
  20. 20. Tounderstandwhat is usedto generatemyK-Score 20
  21. 21. How to improvethe Blackboardportion of yourK-Score 21
  22. 22. How to improvethe GPA portionof my K-Score 22
  23. 23. Even the surveyresponses leadto overallimprovementadvice. 23
  24. 24. Future versions of the studentself service apps will include:• Reminder Services• Planning & Recommendation Services• Advisor Communication and Appointments• Continual, quarterly improvements• And more! Stay tuned! 24
  26. 26. Everyone wants to distort the competitive field 26
  27. 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? 27
  28. 28. “Excuse me. I justwanted to ask aquestion. Whatdoes God needwith a starship?”- Captain Kirk 28
  29. 29. In times of chaos, return to strategy fundamentalsn 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? 29
  30. 30. The value of big data in higher educationn Let’s set other big data research aside • E.g., ‘dark matter’ in DNA, 15 peta bytes, 300 years of computer timen 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 learningn 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? 30
  31. 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? 31
  32. 32. Big data and competitionn What is scarce, difficult or doesn’t scale well? • Data integration, large network effects, brand equity, some content • Exceptional faculty, top executive-managerial talentn 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, geographyn 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 32
  33. 33. How a caterpillar turns into a butterflyn A caterpillar carries genetic material called “imaginal buds” on its underside. It eats a lot and gets fatn 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 caterpillarn In essence, the caterpillar carries, unknowingly, something that will kill it, eat it and become the butterflyn Tell that to your 6-year-old! Who is the caterpillar? Who are the imaginal buds? What the heck is getting hatched? 33
  34. 34. New core competencies and datan Higher education is being forced to develop two new core competencies, previously thought incompatible: • Cost effectiveness • Superior knowledge of the customern 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 fastn 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? 34
  35. 35. We have to change our action model Build, Collect Validate Implement change data model model modelModel A: 6 months – 5 years per cycleBuild-Deploy slowSeek masteryAvoid failureModel B:Learn-Do fastSeek engagement DoEmbrace failure Learn 2 weeks – 3 months per cycle 35
  36. 36. We have to change peoplen Staff • Business process and efficiency excellence • Acumen, knowledge, skillsn Leaders (Deans, VPs, etc.) • Business process and efficiency excellence • Collaboration, people-savvy, culture changing, mountain-movingn 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 conceptsn Boards • Deeper conceptual understanding of the academy • Better knowledge of HE industry competitive dynamics 36
  37. 37. We have to expand our thinkingn Crowd sourced analytics • Within the company • Across the globe?n Super-fast, real-easy data movement • In-memory analytics may change thingsn 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 seeingn Security • New forms of protection, anonymity • Third parties to provide security services? 37
  38. 38. Let’s go surfing now, everyone’s learning how… 38
  39. 39. Thank you!Questions? 39