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Predictive Modeling

Predictive Modeling. The Key to Enrollment Management GISEM Nancy G. McDuff October 22, 2006. What is Predictive Modeling. Predicts the behavior of students How many will enroll? Who will enroll? Who will retain? How much it costs to attract/keep a student? Who will graduate?

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Predictive Modeling

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  1. Predictive Modeling The Key to Enrollment Management GISEM Nancy G. McDuff October 22, 2006

  2. What is Predictive Modeling • Predicts the behavior of students • How many will enroll? • Who will enroll? • Who will retain? • How much it costs to attract/keep a student? • Who will graduate? • What they will study?

  3. Predictive modeling: A short definition Statistical analysis of past behavior to simulate future results. For admitted students, the probability that a student will enroll can be determined by shared characteristics and behaviors of students who have enrolled in the past. From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

  4. Why is it Important • Planning • Space • Academic Service • Auxiliary services • Budgeting — costs and revenue • Setting and meeting goals

  5. Basic Uses of Predictive Modeling • How many offers of admission should be made to enroll a certain size class • How many offers must be made to achieve certain characteristics of the class • How many students will graduate • How many students will attrite • How much scholarship do you need to offer

  6. How do you get Started • What do you need to know (what questions are being asked) • What do you know • What do you wish you know • What is predictable • What predicts • Data, Data, Data

  7. Develop tools and techniques to manage information Decide what to collect Don’t over/under collect Identify where to find it Student app College Board State Determine where to store it Decide how to use it Data

  8. From Data to Strategy • Data are raw material • Information is refined by variable analysis • Residency, demographics • Refined information provides energy sources enabling knowledge • Trends, growth patterns, yields • Knowledge makes it possible to create strategies • Marketing strategies, targeting, yield events

  9. Start with what you know • What characteristics predict well • What do you have historically • What are good correlates • How comfortable are you with statistics

  10. Tips and Secrets • Be Conservative • Three models surrounding the most likely case • Define carefully • Be Consistent • Give them what you know, not always what they ask

  11. Questions affecting the model • What is the optimum tuition charge and enrollment mix • How many seats will you need in a major/school • How many students will live on campus • How many students will drop classes • Should you build a new residence hall

  12. More Advanced Predictive Modeling • ACES Validity Study • Non Cognitive Variables in Admissions • Predicting Demand for Majors • LOGIT model for enrollment

  13. What are good predictors • History is usually a good predictor • Sometimes there are unusual correlates • Must start with archived data or beginning to develop history….but of what • Numbers are good, but percentages are better

  14. Enrollment Example • Enrollment equals • Current enrollment • Less attrition • Less graduating students • Plus new students • Predictive modeling is • Current + changes = New • Or inputs – outputs = Net loss/gain

  15. How to Determine Enrollment Current Enrollment Less Attrition Less Graduates Plus New Students Equals New Enrollment

  16. Predicting Enrollment

  17. Predicting the Freshman Class

  18. So How Many Will Enroll • Did the averages work • What other indicators are there • Housing Contracts • Financial Aid/Scholarships Accepts • Registrations • Meal Contracts

  19. What Do You Predict

  20. Looking at History Different Ways

  21. Are you ready for the next generation of students? • Between 1995 and 2015, 20% more students are projected to enroll in U.S. colleges and universities • 80% of the increase in college-aged students between 1995 and 2015 will be under-represented students • Business week (2004) 40% of the increase in the college age population will be in the bottom income quartile • The South will have the largest growth at 18.7% by 2017-18 • Georgia can expect between 26% and 45% growth in H.S. grads From: Noel-Levitz. “Doing More With Less: Building Efficiencies and Effectiveness into Your Enrollment Management Program”, WICHE “Knocking at the College Door”

  22. Academic reputation Rankings/Selectivity Institution type Size Proximity to home Amenities Quality of student life Safety Personal touch/Relationships Class size & student to faculty ratio Academic programs (study-abroad, learning communities, Honors) Programs of study State and institutional financial assistance Receiving scholarships Campus visits Athletics/Campus Appearance What factors influence college choice/retention?

  23. Challenges facing institutions • Fluctuating economy • Fewer students with the ability to pay for the increasing costs of higher education • Strong scholarship, grant, and need-based aid programs to attract students are becoming more prevalent • Static endowments and state support for higher education From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

  24. Challenges Facing Institutions Cont. • Operating in an increasingly competitive environment • Changing demographics • More aggressive marketing and recruiting by both public and private institutions • More sophisticated marketplace…plans, systems, and advanced tools being developed From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

  25. Can lead horse Models need to be developed over time – numerous years Models can alter by changes in policies Financial aid Tuition Models can be costly – time, accuracy, money Modeling usually is homogeneous (a model for freshmen recruiting usually would not fully apply to transfers.) Challenges of Predictive Modeling

  26. Challenges

  27. Challenges

  28. Challenges

  29. Summary and Conclusions • Modeling is only part of the puzzle. • Use multiple modes of recruitment • Predictive modeling provides a sense of the data pool accuracy – but inputs must be correct • One can leverage enrollment by finances and characteristics

  30. Resources and References • https://ra.collegeboard.com/ Enrollment Planning Services • www.nslc.org/National Student Clearinghouse • http://www.amstat.org/index.cfm?fuseaction=main American Statistical Association • http://www.collegeresults.org/ The Education Trust • https://www.noellevitz.com Noel-Levitz • http://www.airweb.org/ Association for Institutional Research • Hopkins, K. Noel-Levitz. (2003, July). “Building and Developing an Effective Enrollment Management Plan for Colleges and Universities.” National Conference on Student Retention. • Topor & Associates. A Contemporary Approach to Marketing Higher Education.

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