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

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

3-Color WEDGE for Comparisons: Incoming SAT Score Bins < 1000 1000-1100 1100-1200 1200-1300 1300-1400 1400 + NEAIR 2016 (Baltimore) – Better Informed Academic Planning Using Student Flow Models → The “WEDGE” (Brian O’Connor, University at Buffalo )

3-Color WEDGE for Comparisons: First Term QPA Performance 0.0 - 1.0 1.0 - 2.0 2.0 - 2.5 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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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 )

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