1. Powered by CIO Enrollment Management Presentation April 17, 2015

2. What Do We Need to Be Effective Enrollment Managers?

3. Today’s Objectives  Identify gaps in our enrollment strategy  Predict FTES With a Support System  Managing Low and High Enrollment Courses  Identify Course Taking Patterns

4. Enrollment Management Survey We Asked A Few Simple Questions: 30 Respondents Answers: We all need new technologies Nobody is really there yet

5. What else do you want/need?  Managing student pathways, particularly in Gen Ed areas.  Tools for managing class sizes that support the Completion Agenda within current funding formulas  Making EM changes without contention  Multi-year planning tool  Practical strategies for maintaining productivity and increasing enrollment  What does a comprehensive enrollment management plan look like?  It should include elements of attendance accounting, successful outreach efforts, and management strategies.  Scheduling software that integrates facilities use  Best practices in scheduling of accelerated courses for student completion

6. Predicting FTES with a Decision Support System Bob Hughes Director, Enterprise Application Systems Mt. San Antonio College

7. We have the data; the problem is timeliness

8. Timeliness  Plenty of reports showing how we did at the end of the term esp. CCFS-320  Plenty of reports showing how we are doing during the term with regard to FTES  Not enough actionable data during registration - the critical period when we can meet students needs (and capture more FTES) by meeting demand before classes start

9. A Decision Support System  Custom built in-house using Oracle Application Express  No additional license fee (covered by our existing Oracle license)  Resides in the same database as Banner  Built by a researcher temporarily re-assigned as a programmer  Received the Excellence in College Planning Award in 2015 from the RP group

10. Three Horizons 1. What can we do in the current term to better meet student demand? 2. What can we do in the upcoming term to better meet student demand (during registration)? 3. What can we do next year to better meet student demand (schedule build)?

11. Horizon 1: The Current Term

12. Each Dean gets a view specific to her division. The landing page shows a graph containing all classes in the division in four categories of fill rates.

13. Clicking on a color of the graph brings you to a list of sections in that category. This example shows the 45 classes in the 60% - 74.99% range.

14. Built in actions allow you to add or remove columns, add additional filters, and download to Excel

15. 5 standard report formats are available

16. An example of the FTES report by Department. Shows Current FTES vs Projected FTES (4 year weighted average) vs Potential FTES (all sections at 100%)

17. Similar comparison between Divisions

18. Horizon 2: The Upcoming Term

19. What are our ‘hottest’ classes? This report shows classes with fill rates of 90% or more, ordered by how quickly they filled. All 47 sections of ENGL 1C filled in the first 4 days of registration.

20. What demand are we missing? After the classes are full and waitlists are full, we can measure intensity of demand by looking at attempts to register after the class has closed.

21. What classes are lagging or exceeding in their demand? This report shows a comparison of fill rates by day during registration vs prior years and projected fill rates (weighted average). Philosophy is meeting projections.

22. Sociology is lagging both historical demand and projected fill rates.

23. Horizon 3: Next Year

24. FTES Distribution for the year by Division.

25. FTES Distribution for the term by Division.

26. FTES Distribution for the year by Departments in a single Division. Do we need to change the offering pattern (for example – more offerings in Fall vs Spring?)

27. FTES Distribution for each term by Departments in a single Division.

28. Sandbox – a place to experiment with course scheduling decisions before the schedule is built.

29. Start from a baseline of what was offered last year.

30. This section of AMLA 21S didn’t fill as expected. We know why – we expect it to do better next ye ar.

31. Default is projected fill rate. We’ll enter 85%. We can increase this to the projected fill rate (4 year weighted average), or enter our own prediction.

32. User updated courses now show at the top of the list

33. We’re going to add some additional sections. We choose courses where the demand is the greatest. One option is where there was most demand after all sections of the course filled.

34. Second option is where all sections of the course filled quickest during registration.

35. We added another section of PHIL 15.

36. We can also add brand new courses, or courses that are beyond the ‘top three’ in terms of demand.

37. Note that this is listed as ‘User Created’. We can delete these if necessary; the schedule will revert back to the historical schedule.

38. Three changes resulted in a projected increase of 8.3 FTES. We can do several models. Each user has their own “sandbox” to simulate schedules.

39. The Result?  Fall 2013 – demand began to soften  Used the Decision Support System during registration for Spring 2014 to add/cancel classes where warranted, and identify areas where more marketing was needed  Met FTES targets in 2013-14  Mandate to grow 3% from Funded FTES in 2014- 2015  Currently on target to be up 4% for the year

40. Enrollment and Efficiency Santanu Bandyopadhyay Cypress College

41. Nuts & Bolts: The Data Points Trend information  Enrollment/FTES by division  Fill rate by division  WSCH/FTES by division  Unmet demand (unduplicated) by Course  Demand/Supply analysis by course level  Growth opportunities Previous semester  Classroom availability  # Full-time Faculty by department  Class size  Deficit in extended day budget by division

42. Demand Analysis – Trend  Unmet demand (unduplicated) by course: # Unduplicated students who attempted to register for a section PLUS # waitlisted students After adding 8 sections (240 seats), demand for SOC 101 declined by 213 in 2015. For Math 101, after adding 4 sections (120 seats), demand increased by 18. Implications for future planning? Increasing v. flat demand?

43. Demand Analysis – Trend Demand/Supply analysis by course level: # seats offered v. seats occupied for levels of courses Availability of seats and waitlisted students in each level indicate potential for better alignment between demand and supply At 201 to 250 level, nearly 1,200 seats are available although 648 students are waitlisted If perfect alignment is achieved, demand will exceed supply for only 100 level classes

44. Demand v. Contribution Four Quadrants  Green (Q1): High demand, High capacity  Amber (top left) (Q2): High demand, low capacity  Red (Q3): Low demand, low capacity  Amber (bottom right) (Q4): Low demand, high capacity

45. Using 4-Quadrant Grid  Focus on Q1 and Q2 courses for growth  Caveat: Q2 courses may increase deficit in extended day  Q3 are likely 3 rd and 4 th sem classes or new programs: needed for completion and/or transfer. Schedule in alternate semester/year  Potential to grow: Q4 – Outreach Plan?

46. Tying it all together Demand Focus on Unmet Demand Student need upper level classes Capacity Evaluate Physical Capacity Balance online growth with on-campus Efficiency Build capacity by improving efficiency Factor in cost: 4-quadrant guide

47. Schedule Analysis and Marketing for Growth Craig Justice and Kathi Swanson

48. Student-centered Scheduling  Student choices as consumers focus on day of week, time of day, cost, work schedules, transportation issues among others

49. ACE Survey  American Council on Education (ACE) survey indicate that the main forecasting tools used for scheduling are historical course enrollment information and local knowledge rather than indicators of future student demand

50. Newer Tools  Predictive Analytics  Wait List Data  Student Education Plan Data  Social Media Survey Tools  Strategic Marketing

51. Capstones, Math & English  Predictive Analytics  Wait List Data  Student Education Plan Data  Social Media Survey Tools

52. Section Cancellation Analysis

54. Curriculum Analysis  Capstones  Sequences  Bottlenecks

55. Curriculum Analysis

56. Using Data and Tools to Plan  The Challenge: Less Nordstrom Style Scheduling, More Student-Centered Scheduling  Shifting the Course Scheduling Culture  Use good analytical data to inform the plan  Plan the Schedule First, Staff It Second  Marketing: Inform the Customers

57. Completion Dashboards Integrating with Degree Audit to Change Student Behavior and Predict Course Needs JoAnna Schilling, Cerritos College

58. JoAnna Schilling,

59. Frank Mixson, 12345678

62. Frank Mixson, 12345678

65. Debra Moore, 12345678

68. Discussion