Making Sense of Big Data: National Awareness, Local Action

1. Making Sense of Big Data: National Awareness, Local Action Dr. Linda L. Baer Dr. Ann Hill Duin July 25, 2012

2. Objectives • Big Data on the National Level • National examples • What does it mean for you? • Local actions • What are implications for your institution? • Solution Tools • Concrete next steps • Q & A

3. Implementing analytics and applying it to make data driven decisions is a major differentiator between high performing and low performing organizations. Big Data: The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute 2012.

4. Definitions of Analytics • Academic analytics combines large data sets, statistical techniques and predictive modeling to produce “actionable intelligence.” Campbell, DeBlois, and Oblinger. 2007. EDUCAUSE review July/August. • Action analytics produces actionable intelligence, service-oriented architectures, mash-ups of information/content and services, proven models of course/curriculum reinvention, and changes in faculty practice that improve performance and reduce costs. • Analyticsis about pursuing the improvement and optimization of institutional performance along all dimensions. Norris, Donald, Linda Baer, Joan Leonard, Lou Pugliese, Paul Lefrere, “Action Analytics: Measuring and Improving Performance That Matters,” EDUCAUSE Review, Jan/Feb 2008.

5. Disrupting and Transformative Analytics • Accountability Analytics – external policy makers and publics set standards for accountability and comparability, individual institutions judged according to performance • Performance Measurement and Improvement Analytics – To meet accountability standards institutions must embed analytics into their processes and continuously strive to measure and improve performance.

6. Big Data • Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. The ability to store, aggregate and combine data and then use the results to perform deep analysis is becoming a reality. Big Data: The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute. 2012

8. Transformational Potential of Analytics • Creating transparency • Enabling experimentation to discover needs, expose variability, and improve performance • Segmenting populations to customize actions • Replacing/supporting human decision making with automated algorithms • Innovating new business models, products and services. Big Data: The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute. 2012.

9. What’s happening at your institution?

10. National Examples

11. National Examples • Virginia Community Colleges are actively engaged on high schools campuses to advise, recruit and prepare students for successful college entrance. • University of Michigan utilizes Strategic Enrollment Management to identify at risk students and to provide mentoring and support services that have improves the success of these students dramatically. • Structural realignment to eliminate bottlenecks in course and program progressions and unreasonable prerequisites. • Designing curriculum around a full summer semester increased the timely completion for students at BYU-Idaho and University of Northern Texas.

12. Using predictive analytics to shape policies and practices including limiting the number of credits lost during transfer, strict policies on withdrawal and academic progress. • Purdue’s Signals program, which has been productized by SunGard, is the best known example of embedded, predictive course analytics. It produces red, yellow and green evaluations of student behaviors in comparison with past behavior of successful students. • Northeastern University has adapted Salesforce.com to create a sort of LRM system for advancing student success; • Sinclair Community College has developed the Student Success Plan (SSP), a case management and intervention software system which it is turning into an open-source product with a community of practice of users at institutions deploying this holistic advising utility;

13. Arizona State University’s eAdvisor System enables predictive analytics-enabled evaluation of student behavior and learner tracking against norms; • Capella University’s learning objective mapping system provides guidance for each student and is at the heart of their competence-based approach to learning and student success; • Rio Salado’s Student Success Model monitors each student’s progress/success/at-risk indicators; SOS, Status of Students model which took warning levels to a weekly basis using frequency of student log in, site engagement, and pace in completing course as indicators • Retention systems and services such as those offered by Starfish and EBI/MAPWORKS utilize many LRM-like features;

14. Norris and Baer FrameworkOptimizing Student Success through Analytics • Manage the Student Pipeline • Eliminate impediments to student retention • Use predictive analytics to assess and respond to student behavior • Evolve the learner relationship management system • Create personalized student learning environments • Engage in large-scale data mining • Extend to life long learning; learning, work and life success Donald Norris and Linda Baer. 2012. A Toolkit for Building Organizational Capacity in Analytics

16. Game Changers • Education changes lives and societies, but can we sustain the current model? New models and new technologies allow us to rethink many of the premises of education—location and time, credits and credentials, knowledge creation and sharing http://www.educause.edu/research-publications/books/game-changers-education-and-information-technologies

17. What should or might happen?

18. Local Examples

19. Local action:BI and academic analytics at UMN • Enterprise strategy and goals • Multiple directions and offices • Distinction between academic and learning analytics • New paradigm • iSEAL at UM Rochester • Use of Google scripts at UM Duluth

20. Enterprise strategy and goals • A shared reporting strategy for the entire University. • A shared data platform that is a common home for institutional and unit data. • A shared understanding of key metrics, data definitions and appropriate use. • A set of shared tools providing an enterprise platform for data analysis and reporting. • A shared development environment enabling units to share knowledge, ideas, and reports.

21. Business Intelligence tool (IT) DELIVERY COLLABORATION CONTENT MANAGEMENT SharePoint Server SEARCH Reports Dashboards Excel Workbooks Analytic Views Scorecards Plans END USER TOOLS & PERFORMANCE MANAGEMENT APPS Excel PerformancePoint Server BI PLATFORM SQL Server Reporting Services SQL Server Analysis Services SQL Server DBMS SQL Server Integration Services

22. Strategic Altitude 30,000 25,000 15,000 Ground UMN Data Governance Roles and Responsibilities Executive Strategic Level: Why we have Data Governance Training, Communication and Change Management Execution and/or Decision Path Tactical Level: How Data Governance will be implemented Operational Level: Where people work with data

23. Analytics Collaborative (Core and Extended) Academic Units Business Area(s) Reporting Professional Partners Extended Virtual Team Service Request Process Change Management Enterprise Architect Colleges Program Management BI Implementation Committee Core Team IT Strategic Planning End Users Project Facilitation Business Strategic Planning Incident Management Academic Support Resources Finance Training Knowledge Management Human Resources Process Management

25. Institutional level

26. From Reports to Analytics

27. Analytics in Education • Institutional: Academic Analytics • Coarse-grained, multidimensional data, such as first-term GPA, ACT, home state, … by student • Collected by administration • Smallest grain: course • Curriculum: Learning Analytics • Fine-grained data , such as a grade on a quiz, attendance data, visits to course web site,… • Collected by faculty • Smallest grain: “nano-data” Academic Analytics and Learning Analytics distinctions: Long, P. and G. Siemens. 2011. Penetrating the Fog. EDUCAUSE Review.

28. Bioinformatics Methods • Clustering to identify groups of students who differ in their success • Personalized approach • Decision trees to identify factors important to success • Algorithms to develop college specific approaches

29. The paradigm: Individualized medicine Claudia Neuhauser (2012). From Academic Analytics to Individualized Education. In Duin, A.H., Nater, E.A., Anklesaria, F. (Eds.). Cultivating change in the academy: 50+ stories from the digital frontlines at the University of Minnesota in 2012. University of Minnesota. http://purl.umn.edu/125273.

30. Qualities and Outcomes • From data to knowledge to action • Resources toward analysis and less toward user-friendly interfaces • Tailored reports pushed to colleges and coordinate campuses • Performance • Scenarios • Predictive models • Basis for decision making • Central resource • Consistent information and knowledge • Cost-effective: small group can provide results; no need to do analysis in each college individually • Dashboard • Recommender system • Alerts • Accreditation • SLOs • Evidence-based decision making

31. UM Rochester’s iSEALintelligent system for education, assessment, and learning • Comprehensive data collection tool that tracks all student activity to provide a deep data set for learning analytics • Sharable, reusable materials – tagged with concepts and learning objectives – accessible to students at all times

33. Custom assessment and feedback application Abram Anders. (2012). Creating Custom Learning Assessment and Student Feedback Applications with Google Apps Script. In Duin, A.H., Nater, E.A., Anklesaria, F. (Eds.). Cultivating change in the academy: 50+ stories from the digital frontlines at the University of Minnesota in 2012. University of Minnesota. http://purl.umn.edu/125273.

34. Download at http://conservancy.umn.edu/handle/125273Wordpress site for interaction is at https://cultivatingchange.wp.d.umn.edu/ Twitter hashtag is #CC50

35. Your next step?

36. Q & A Contact Us: Lindalbaer@yahoo.com ahduin@umn.edu

37. References • Adams, Bernadette. 2012. Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief April 10, 2012 http://evidenceframework.org/wp-content/uploads/2012/04/EDM-LA-Brief-Draft_4_10_12c.pdf • Borner, Katy. 2012. LAK12 Keynote Visual Analytics in Support of Education. Second Annual International Conference on Learning Analytics and Knowledge. Vancouver, British Columbia. • Davenport, Thomas. Jeanne Harris and Morison. Analytics at Work. 2010 • Ferguson, Rebecca. 2012 The State of Learning Analytics in 2012: A Review and Future Challenges. Technical Report KMI-12-01, March 2012. • Grajek, Susan. 2012. Research and Data Services for Higher Education Information Technology: Past, Present, and Future. EDUCAUSE Review November/December 2012. http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume46/ResearchandDataServicesforHigh/238391 • Lavalle, Steve. Michael S. Hopkins, Eric Lesser, Rebecca Shockley, and Nina Kruschwitz. 2010. Analytics: The New Path to Value. MIT Sloan Management Review. http://cci.uncc.edu/sites/cci.uncc.edu/files/media/pdf_files/MIT-SMR-IBM-Analytics-The-New-Path-to-Value-Fall-2010.pdf • Long, Phil and George Siemens, 2011. Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review September/October 2011. • http://net.educause.edu/ir/library/pdf/ERM1151.pdf • Manyika, James. Michael Chui, Brad brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh and Angela Hung Byers. 2012. Big Data: The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute. http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation • Norris, Donald and Linda Baer. 2012. A Toolkit for Building Organizational Capacity. In process. • Oblinger, Diana, Editor. 2012. Game Changers: Education and Information Technologies. EDUCAUSE. Educause.edu/books • van Barneveld, Angela, Kimberly E. Arnold, and John P. Campbell. 2012. Analytics in Higher Education: Establishing a Common Language, ELI Paper1:2012. http://net.educause.edu/ir/library/pdf/ELI3026.pdf • Winning by Degrees: The Strategies of Highly Productive Higher Education Institutions McKinsey Global Institute http://mckinseyonsociety.com/downloads/reports/Education/Winning%20by%20degrees%20execsum%20v5.pdfReferences