1. Better Informed Academic Planning Using Student Flow Models
System Development Team:
Brian J. O’Connor
Assistant Vice President for Data Analytics
University at Buffalo – Institutional Analytics
Michael D. Stratford
Associate for Data Analytics and Visualization

2. A brief summary of our institution
● Premier, research-intensive public university
● Member of the Association of American Universities (AAU)
● Largest, most comprehensive institution in the 64-campus SUNY system
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

3. A brief summary of our institution
● Premier, research-intensive public university
● Member of the Association of American Universities (AAU)
● Largest, most comprehensive institution in the 64-campus SUNY system
Some pertinent facts related to this presentation:
● 12 Schools/Colleges
Arts & Sciences, Architecture, Dental, Education, Engineering, Law School,
Management, Medicine, Nursing, Pharmacy, Public Health, Social Work
● Nearly 30,000 Students (~ 2:1 ratio of UG:GR, Freshmen Cohort = 4,000)
● More than 110 Undergraduate Degree Programs
● First Year Undergraduate Retention = 88%
● Six Year Undergraduate Graduation Rate = 72%
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

4. Undergraduate Student FLOW Tracking → IN, OUT, BETWEEN
Admissions
Cohorts
(IN)
1st Year
2nd Year
3rd Year
4th Year
5th Year
6th Year
IN = Admissions (predictive models)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

5. IN = Admissions (predictive models)
Undergraduate Student FLOW Tracking → IN, OUT, BETWEEN
Admissions
Cohorts
(IN)
1st Year
2nd Year
3rd Year
4th Year
5th Year
6th Year
Leaving Campus w/o Degree or Graduating (OUT)
IN = Admissions (predictive models)
OUT= Leave without degree or graduate
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

6. IN = Admissions (predictive models)
Undergraduate Student FLOW Tracking → IN, OUT, BETWEEN
Admissions
Cohorts
(IN)
Changing Majors Department to Department (BETWEEN)
1st Year
2nd Year
3rd Year
4th Year
5th Year
6th Year
Leaving Campus w/o Degree or Graduating (OUT)
IN = Admissions (predictive models)
OUT= Leave without degree or graduate
BETWEEN= Changing majors during career
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

7. IN = Admissions (predictive models)
Undergraduate Student FLOW Tracking → IN, OUT, BETWEEN
Admissions
Cohorts
(IN)
Changing Majors Department to Department (BETWEEN)
1st Year
2nd Year
3rd Year
4th Year
5th Year
6th Year
Leaving Campus w/o Degree or Graduating (OUT)
IN = Admissions (predictive models)
OUT= Leave without degree or graduate
BETWEEN= Changing majors during career
Focus of the WEDGE
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

8. What is the WEDGE? Three Key Components:
(1) An extensive custom-built dataset combining twenty years of raw disparate data on admissions, demographics, individual term metrics, course histories, degrees … and more.
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

9. What is the WEDGE? Three Key Components:
(1) An extensive custom-built dataset combining twenty years of raw disparate data on admissions, demographics, individual term metrics, course histories, degrees … and more.
(2) A set of analytics tools which combine the base data components in countless ways to gain a more detailed understanding of the relationships between various raw or combined metrics.
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

10. What is the WEDGE? Three Key Components:
(1) An extensive custom-built dataset combining twenty years of raw disparate data on admissions, demographics, individual term metrics, course histories, degrees … and more.
(2) A set of analytics tools which combine the base data components in countless ways to gain a more detailed understanding of the relationships between various raw or combined metrics.
(3) A data visualization tool built using Tableau for viewing patterns in the raw data or in the analytics summaries, to help “see” what has happened and to help plan for actions to change patterns.
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

11. Primary Purpose: Deliver ACTIONABLE INTELLIGENCE
What is the WEDGE?
Three Key Components:
(1) An extensive custom-built dataset combining twenty years of raw disparate data on admissions, demographics, individual term metrics, course histories, degrees … and more.
(2) A set of analytics tools which combine the base data components in countless ways to gain a more detailed understanding of the relationships between various raw or combined metrics.
(3) A data visualization tool built using Tableau for viewing patterns in the raw data or in the analytics summaries, to help “see” what has happened and to help plan for actions to change patterns.
Primary Purpose: Deliver ACTIONABLE INTELLIGENCE
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

12. Why was the WEDGE Developed?
Why Study Student Flow?
Why was the WEDGE Developed?
General Perspective – Impacts on Entire University
▪ Understand and Improve Overall Student Retention
▪ Understand and Improve Overall Graduation Rates
▪ Improve Ability to Guide Student Paths via Advising
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

13. Why was the WEDGE Developed?
Why Study Student Flow?
Why was the WEDGE Developed?
General Perspective – Impacts on Entire University
▪ Understand and Improve Overall Student Retention
▪ Understand and Improve Overall Graduation Rates
▪ Improve Ability to Guide Student Paths via Advising
Specific Perspective - College of Arts and Sciences
▪ Understand CAS Departures and Minimize Them
▪ Understand Switches to CAS and Maximize Them
▪ Pro-active Course Planning based on Major Schools
▪ Predict Service vs. Major Credit Hours (and Revenues)
▪ Must Balance Student Outcomes with CAS Enrollments
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

14. The WEDGE Student Flow Visualization – 3 Primary Status Options
LEFT w/o Degree
Still HERE
LEFT with Degree
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

15. The WEDGE Student Flow Visualization – 3 Primary Status Options
LEFT w/o Degree
Each stacked bar shows the percentage of the initial cohort who have a particular status at a point in time during their undergraduate career, from admissions (term 0) through their 13th term.
Still HERE
LEFT with Degree
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

16. The WEDGE Student Flow Visualization
Original Usage Option:
Select a cohort subgroup, then see how they flow through the UG pipeline. Select filters include:
▪ Admissions Major School
▪ SAT, ACT or QPA Bins
▪ Adm Type (New/Transfer)
▪ High School / College
▪ Different Cohort Years
▪ Many, Many Others …
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

17. The WEDGE Student Flow Visualization
After that selection is made, the system will automatically generate the term-by-term status and metrics for visualization and further analysis.
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

18. The WEDGE Student Flow Visualization – Additional Levels of Detail
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

19. The WEDGE Student Flow Visualization – Additional Levels of Detail
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

20. The WEDGE Student Flow Visualization – Additional Levels of Detail
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

21. The WEDGE Student Flow Visualization – Additional Levels of Detail
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

22. The WEDGE Student Flow Visualization – Additional Levels of Detail
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

23. 3-Color WEDGE for Comparisons: Entire Cohort for Multiple Years
Fall 2008
Fall 2009
Fall 2010
Fall 2011
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

24. 3-Color WEDGE for Comparisons: Various School Multi-Year Patterns
Arts and Sciences
Engineering Approved
Engineering Intended
School of Management
Undecided Majors
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

25. 3-Color WEDGE for Comparisons: Incoming SAT Score Bins
< 1000
1400 +
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

26. 3-Color WEDGE for Comparisons: First Term QPA Performance
2.5 – 3.0
3.0 – 3.5
3.5 – 4.0
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

27. 1st Year ATTRITION Switched Majors Continuing Majors
The WEDGE Student Flow Visualization – Impact Metrics
Another Usage Option:
Working backwards, what possible factors may have impacted student outcomes?
1st Year ATTRITION
Switched Majors
Continuing Majors
Undergraduate Career Term Number (Admissions to 13th Term)
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

28. The WEDGE Student Flow Visualization – Impact Metrics
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

29. The WEDGE Student Flow Visualization – Impact Metrics
With knowledge of these types of factors which may trigger early career departures, or changes in major, what pre-emptive ACTIONS can be implemented to better guide students’ career paths for success?
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

30. So, how could the College take advantage of this information to both enhance student outcomes and enhance College enrollments (and revenue)?
One option is to re-direct certain populations of students into the College to complete their degrees, rather than having these students leave the University w/o degree.
One such population was identified using the WEDGE, and campus actions have been initiated to better serve these students, giving them more likely paths to success.
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

31. Contrasting Major Scenarios – Growing or Shrinking Counts over Time
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

32. A Win-Win Opportunity for Student Success and CAS Revenues
Engineering Approved
Engineering Intended
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

33. A Win-Win Opportunity for Student Success and CAS Revenues
By 5th Term of Undergraduate Career:
▪ Similar percent of students are still enrolled
▪ 62% of approved are still in Engineering
▪ Only 39% of intended are still in Engineering
▪ 25% of intended cohort are now CAS majors
▪ Percent leaving w/o degree grows larger for
intended after this point in their UG careers
Approved
Intended
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

34. A Win-Win Opportunity for Student Success and CAS Revenues
By 5th Term of Undergraduate Career:
▪ Similar percent of students are still enrolled
▪ 62% of approved are still in Engineering
▪ Only 39% of intended are still in Engineering
▪ 25% of intended cohort are now CAS majors
▪ Percent leaving w/o degree grows larger for
intended after this point in their UG careers
After 6 Years (in the 13th Term of Career):
▪ Nearly 70% of approved have graduated
▪ Only about 50% of intended have degrees
▪ About 1 in 5 intended have degrees in EAS
▪ About 1 in 5 intended have degrees in CAS
▪ 45% “red bar” is one of highest rates at UB
Approved
Intended
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

35. A Win-Win Opportunity for Student Success and CAS Revenues
By 5th Term of Undergraduate Career:
▪ Similar percent of students are still enrolled
▪ 62% of approved are still in Engineering
▪ Only 39% of intended are still in Engineering
▪ 25% of intended cohort are now CAS majors
▪ Percent leaving w/o degree grows larger for
intended after this point in their UG careers
After 6 Years (in the 13th Term of Career):
▪ Nearly 70% of approved have graduated
▪ Only about 50% of intended have degrees
▪ About 1 in 5 intended have degrees in EAS
▪ About 1 in 5 intended have degrees in CAS
▪ 45% “red bar” is one of highest rates at UB
Taking early actions, based on greater understanding of key student outcome indicators could improve retention in all years of the pipeline, and allow more students to successfully get degrees.
Approved
Intended
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

36. Beyond that understanding … here are recent UG cohort patterns
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

37. A Win-Win Opportunity for Student Success and CAS Revenues
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

38. Demonstration of Some Live WEDGE Tools
Where from?
Where to?
Dynamic WEDGE with user selections
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

39. Questions?
NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )