Presentation Transcript

1. Sherpa, MAP, Predictive Analytics and Dashboard: Software Designed for Success

   Dr. Bob Bramucci, Vice Chancellor, Technology and Learning Services Jim Gaston, District Director of IT – Academic Systems
2. About Us About the SOCCCD Two-college district in southern California Approximately 41,000 students (23,000 FTES) Two Colleges, Three Campuses Saddleback College in Mission Viejo Irvine Valley College in Irvine Advanced Technology and Education Park (ATEP) in Tustin
3. SOCCCD Student Success Systems MAP Sherpa Student Dashboard Predictive Analytics
4. The Problem
5. The Planets Align…
6. Progress So Far
7. Technology & Productivity
8. Moore’s Law
9. Computer Prices
10. Communications Speed
11. Search Time
12. Computers often exhibit Exponential rather than Arithmetic efficiencies
13. Disinvestment by States
14. We Can’t Get There Alone 80-90% spent on salaries + benefits in K-12 and postsecondary education limit options (we can’t realistically double the number of people) Declining state investment Economic history shows that technology often produces exponential changes Is increased automation the answer?
15. SOCCCD’s SECRETS It’s not just what we created (an integrated suite of student success software applications) It’s How We Built It.
16. SOCCCD’s Secrets Student-Centered Design Agile Software Development Methods Inclusive Flexible Human-Centered Allocation of Function
17. Student-Centered Design
18. Agile Software Development
19. Allocation of Function Vs.
20. Fitts’ List HUMANS Complex pattern recognition and generalization Judgment under uncertainty MACHINES Rote calculation Routine, repetitive, precise movements
21. Human-Centered Allocation of Function We believe that simply replacing people with machines is NOT the best answer Rather, we can allocate functions between humans and machines in a strategic way that allows people to leverage their unique human strengths--a “human-centered design” approach
22. SOCCCD Student Success Systems MAP Sherpa Student Dashboard Predictive Analytics
23. My Academic Plan (MAP)
24. MAP Project Goals Create a system that provides: Self-service guide for students to develop academic plans Tracking system for counselors and students to monitor progress toward goals Automated assistance to help students select classes that achieve their goals Integration with Project ASSIST to reduce data maintenance and increase accuracy
25. Process Design Team Counselors Students Articulation Staff Timeline Discussions began in late 2004 Development began in Fall 2005 MAP went online on April 27, 2007 Technology Service Oriented Architecture (SOA) Integration with Project ASSIST
26. Usage Statistics - Plans Created From: 4/27/07 to 2/23/14
27. MAP Demonstration
28. Sherpa
29. Sherpa So what is Sherpa? Sherpa is a recommendation engine that guides students to make informed decisions regarding courses, services, tasks and information.
30. Sherpa Architecture Timing Event Trigger Courses Services Information Tasks Transcript GPA Ed Goal Reg Activity Current Classes Status Recommendations Student Profile Nudge Feedback - Preferences Feedback - Ratings Portal News Feed Email Text To-Do List Mobile
31. Let’s translate this into the real world… Profile Nudge Here is some advice for you… ?
32. What makes a nudge, a nudge? Timely Personal Relevant Actionable
33. Recommendation Examples
34. Phased Approach Phase One – Closed Class Assistance Assists students in finding a replacement class during registration Phase Two – Profiles and Nudges Announcements, Email, SMS, Voice-To-Text Phase Three – Portal Refresh Calendar, To-Do List and News Feed Phase Four – Mobile In beta release Phase Five – Predictive Analytics In development Phase Six – Student Success Dashboard Beginning in Spring 2014
35. Some Promising Early Results Closed Class Assistant: Has directly resulted in over 20,000enrollments since October 2010. Matriculation Pilot: Probation numbers dropped 39% one month after Sherpa notification.
36. Results: More Engagement Yesterday I gave an IVC “Freshman Advantage” presentation to almost 100 high school Seniors at Segerstrom High school. I have been giving these presentations for years. I cannot BEGIN to tell you the positive difference in the student’s behavior due to the Sherpa nudge. It is like they had been touched by an electrified cattle prod in a GREAT way! Students were literally crowded around me before the presentation SHOWING me the Freshman Advantage email message on their smart phone!!! They had a higher level of awareness, a feeling of more urgency to complete matriculation and a better understanding of the process. - Irvine Valley College Outreach Specialist
37. Sherpa Demonstration
38. Predictive Analytics
39. Uses Early Alert Course Recommendation Adaptive Learning
40. Machine Learning Decision trees Neural networks Bayesian networks Learning Automata Support Vector Machines
41. Three Math Models Global: is student predicted to fail any of his/her courses? Specific: A,B,C,D,F grade prediction for any particular class a student might take Regression Model
42. Data Scientists
43. Problem Structure Millions of movies—but you’ll only view a small subset Thousands of courses—but the average student will only take a small subset
44. Matrix FactorizationModel
45. Student Success Dashboard
46. Focus on Student Success
47. Next Steps Research Scaling Up Additional success modules
48. Our Roadmap
49. Questions? Bob Bramucci: rbramucci@socccd.edu Jim Gaston: jgaston@socccd.edu http://www.socccd.edu/sherpa