1. Optimizing Student Success through Analytics Fall Planning Retreat August 13-14, 2012

2.  Big Data and Analytics  Analytics and Optimizing Student Success  Organizational Capacity for Analytics  What do we care about?  How are we doing?  Focus: Student Engagement Project  Students  Faculty & staff  Institution  Next Steps

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

7.  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.

8.  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 Learner Relationship Management 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.

9.  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.

10.  Goals of Arizona State University 2002: Increasing graduate numbers Graduation rates Freshman retention rates Expand ethnic and economic diversity  Outcomes in 2011: Increased enrollment 30% in 10 years Increased minority enrollment as % of total population by 52% Increased degrees awarded by 52% Increased 6-year grad rate by 19% Increased freshman persistence to 84% up 9%  Solutions: Comprehensive use of analytics http://net.educause.edu/ir/library/pdf/ERM1241P.pdf

11. “We found that by applying analytics through every level of the enterprise we have infinitely more information to allow us to help students become successful.” President Michael Crow

13.  Academic performance measures  Student enrollment management  Student learning and engagement  Extended learning and maximizing opportunities in the metro area  Civility and diversity  Others

14.  Seek to understand the student success and engagement environment  Environmental scan  National benchmarks  MnSCU Universities  Inventory of best practices  MSU Mankato – deeper assessment of results  Recommendations

15.  Campus leadership is committed to improving student success and engagement.  Wide variation exists among the MnSCU Universities in approaching engagement and student success as well as active and applied learning  Students can be more engaged and more challenged in the classroom  Faculty have a great opportunity to increase active and applied learning in the classroom  Investment and sustainability in faculty, staff, tools, skills and assessment critical

16.  Levels of academic challenge  Active and collaborative learning  Student/faculty interaction  Enriching educational experiences  Supportive campus environment

17.  Pre-Admission  First Year  Second Year  Third Year  Fourth Year  Transfer http://www.gvsu.edu/cms3/assets/C70BC1A8-9846-C603- 2E5C91853888BC53/BueprintGraphic_9.03.08.pdf

18. High performing institutions have a “persistent restlessness” about improving student success.

21.  Managing the pipeline – Student Enrollment Management  Eliminate barriers to retention and student success  Explore and identify dynamic analytics to respond early to at-risk behavior  Evolve learner relationship management systems– tools for student success  Assess the organizational capacity to develop improved data analytics  Extend student success to life long learning, career/workforce and life success