Welcome to the Era of Big Data and Predictive Analytics in Higher Education

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1 Welcome to the Era of Big Data and Predictive Analytics in Higher Education Ellen Wagner WICHE Cooperative for Educational Technologies Joel Hartman University of Central Florida

2 The Focus of this Session This session will present an introduction to the emerging and evolving topics of Big Data and predictive analytics particularly as they apply to higher education and the use of data to improve student persistence and outcomes. An overview of Big Data, an introduction to the Predictive Analytics Reporting (PAR) Framework, and an institution s perspective on these issues along with their implementation of analytics will be presented.

3 Postsecondary Education and the New Normal Unprecedented demands for Accountability, Efficiency, Effectiveness Increased expectations for greater transparency A recognition that shared services are more than just a good idea that somebody else should do More competition than ever before.

4 We Can Run But We Can t Hide New Approaches to the New Normal: 2012 Higher Education Legislative Recap in the West (Nov 27, 2012) (http://www.wiche.edu/info/publications/pi-2012policyinsights ) Notable issues: postsecondary finance, including attempts to implement a new wave of outcomes-based funding; completion, accountability and major governance changes. Specific issues include adult learners, workforce development, and the implementation of Common Core Standards. Tight budgets will continue to impact higher ed leading to an increased focus on productivity and flexibility for institutions and students

5 Costs increase and completion rates Graduation rates at 150% of time yr colleges 4-yr colleges Cohort year Source: New York Times; NCES

6 The need to flip the curve 90% of community colleges in 2010 and 69% in 2011 Additional 300k to 1 million credentials needed per year Demands of globalized, information economy Rising expectations Higher enrollments More completions Deeper learning outcomes Constrained resources Limited seat capacity Budget cuts Declining family ability to pay 32% of community college students unable to enroll in classes; CA turning away up to 670k students per year 58% of community college budgets cut in ; 41% of cuts >5%; long-term competition with healthcare Student load debt now greater than all consumer loan debt Source: 2011 Community Colleges and the Economy, AACC/Campus Computing Project, April 2011; Community College Student Survey, Pearson Foundation/Harris Interactive, Field dates: September 27th through November 4th, 2010

7 Innovation and Educational Transformation The term innovation derives from the Latin word innovare "to renew or change." Innovation generally refers to the creation of better or more effective products, processes, technologies, or ideas that affect markets, governments, and society. Technologies frequently featured in today s mix of solutions for solving problem and promoting innovation

8 Tech Trend and Analytics Data Warehouses and the Cloud make it possible to collect, manage and maintain massive numbers of records. Sophisticated technology platforms provide computing power necessary for grinding through calculations and turning the mass of numbers into meaningful patterns. Data mining uses descriptive and inferential statistics moving averages, correlations, and regressions, graph analysis, market basket analysis, and tokenization to look inside those patterns for actionable information. Predictive techniques, such as neural networks and decision trees, help anticipate behavior and events.

9 Why the Emergence of Big Data? Expectations for accountability to stakeholders Demands for evidence to guide and support decision-making Finding metrics that matter to institutions AND individuals Technology platforms provide a means to the end.

10 Where are we headed? Business Models Provide Guidance Courtesy Phil Ice

11 Big Data and Analytics and Frameworks, Oh, My

12 BIG DATA AND ANALYTICS ARE TAKING HIGHER EDUCATION BY STORM

13 Where to Begin????? Uncertainty about where to start No established industry best practice about what to measure No established industry best practice around methodology Institutional Culture, Learning Culture and Status Quo Enterprise concern about what the data will show Competing priorities and lack of incentive for collaboration between different groups Siloed data across the enterprise doesn t help. 13 Sage Road Solutions LLC

14 Evidence-based decision-making Success and decision making are predicated on access to data Understanding strengths and weaknesses is dependent on having access to all data within the enterprise Data tells us what has happened and improves strategic planning moving forward 14

15 What is the PAR Framework? A big data analysis effort identify drivers related to loss and momentum and to inform student loss prevention WCET member institutions voluntarily contribute de-identified student records to create a single federated database.

16 Making Data Matter Gather the data Turn the data into information Use the information to help learners

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20 Institutional Partners American Public University System* Ashford University Broward College Capella University Colorado Community College System* Lone Star College System Penn State World Campus Rio Salado College* Sinclair Community College Troy University University of Central Florida University of Hawaii System* University of Illinois Springfield* University of Maryland University College University of Phoenix* Western Governors University

21 Predicated on a framework of common data definitions Common data definitions at the foundation of reusable predictive models and meaningful comparisons. Common data definitions openly published via a cc license https://public.datacookbook.com/public/instit utions/par

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23 Multiinstitutional data Institutional Data 33 Variables and common definitions from POC College Data Program Data Classroom /Instructor Data Studen t Data >70 variables and growing during implementation LMS DATA

24 Making Data Matter Via Modeling Model building is an iterative process Around 70-80% efforts are spent on data exploration and understanding. 24

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26 What are we going to DO with what we ve learned????

27 Validated Multi- Institutional Dataset Some of PAR s Products Reflective Report Benchmark Reports Aggregate Models Institutional Models Student Watch List Policy Local Intervention Comparative Interventions

28 Actionable Predictive Models

29 PAR Student Success Matrix Powerful Framework for benchmarking student services and interventions

30 Quantified Quantification intervention of Intervention effectiveness Effectiveness Quantified intervention effectiveness results results

31 ACT PREDICT Reusable predictive models Student level watch lists for targeted interventions Measurable results RESULTS Common Definitions of Risk Common Definitions of interventions Multi-Institutional collaboration Scalable cross-institutional improvements

32 Partner Perspectives: The University of Central Florida Dr. Joel Hartman Vice Provost for Information Technologies and Resources and CIO

33 THANKS for your interest

34 Big Data & UCF Student Success 1

35 From Data To Information Era Evolutionary Step Technologies Perspective 1960s- 1970s Data Collection Computers, tapes, disks Retrospective, static data delivery 1980s Data Access RDBMS, SQL, ODBC Retrospective, dynamic data delivery 1990s Data Warehouses, Data Marts, Decision Support Tools, BI Data warehouses, data marts, OLAP Retrospective, dynamic data delivery 2000s Data Mining / Big Data Models, algorithms, fast computers, massive databases, dashboards Prospective, proactive information delivery, visualization, and exploration Source: Kurt Thearling

36 An Information Architecture Policy, security, technology infrastructure, software, and people Hierarchy of users and information needs Hierarchy of tools and methods Full-service to self-service support In support of information-driven planning and decision making

37 Analytics / Data Science The extraction of hidden predictive information from large databases determination of rules working in the target environment, but hidden in the data future events, trends, behaviors can tag individuals predictive capabilities

38 Barriers Lack of executive vision or familiarity Inability to associate important business problems with big data solutions Users or executives rooted in a retrospective or green bar mentality Cost No data warehouse or analytical tools Data quality issues Uncollected data cannot be analyzed

39 UCF Information Architecture 6

40 Student Success Initiative Goals Increase student completion rates Reduce time to degree Minimize excess credit hour accumulation 7

41 PeopleSoft Degree Audit Mapping & Tracking BIG DATA DEGREE PROGRAM SUPPORT PROGRESS Core Services Intervention Support Programs Intervention Academic Support Programs Intervention INTERVENTIONS P.R.O.G.R.E.S.S. Probing to Remove Obstacles toward Graduation and Retention for Enrolled Student Success 8

42 Different Levels of Insight Descriptive Analytics 1. How many logins, page views, and other metrics have occurred over time? 2. What were the course completion rates for a particular program over time? What were the attributes of the students who didn t successfully complete? 3. Which tools are being used in courses the most? Predictive Analytics 1. Which students are exhibiting behaviors early in the semester which put them at risk for dropping or failing a course? 2. What is the predicted course completion rate for a particular program? Which students are currently at risk for completing and why? 3. Which tools and content in the course are directly correlated to student success? 9

43 Civitas Learning and PAR Project: Insights from Big Data Translate complex data into real-time, personalized recommendations to inform decisions and interventions that lead to student success 10

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49 Big Data & UCF Student Success 16

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