Understanding the student lifecycle

University of Central Florida forecasts enrollment, improves planning, supports accurate decisions

Knowledge forms the cornerstone of any institution of higher education. Beyond the lecture halls, classrooms and libraries of universities the world over, knowledge is at the very heart of university operations. Driven by institutional research (IR) departments – which play a vital role in supporting internal and external consumers of data – accurate and sophisticated analysis provides institutional decision makers with the insights needed to make proactive decisions.

The University of Central Florida (UCF) is a major metropolitan research university and is the second-largest university in the US. Its Office of University Analysis and Planning Support, along with the Operational Excellence and Assessment Support and Institutional Knowledge Management groups, comprise the backbone of the school's institutional knowledge.

The Office of University Analysis and Planning Support uses SAS® Business Analytics for data management, analysis and information delivery across the organization. The tecnhology is used by UCF to analyze and report on its colleges, programs and student life cycles, including admissions, course-taking behavior, retention, and completion and continuation patterns. The department also conducts research on various areas of university management, such as faculty activity, cost studies, university benchmarking, resource utilization and salary equity.

Better support for students

Using the institution’s data warehouse – managed by the Institutional Knowledge Management group – the Office of University Analysis and Planning Support uses SAS Analytics to provide university leaders with the information needed to support decision making in four main areas: enrollment planning, strategic planning, management analysis and exploratory analysis.

"The university's customers are its students. Our analytical work helps the leadership team make informed decisions to further UCF's mission of providing students with a world-class education," says Sandra Archer, PhD, Director of the Office of University Analysis and Planning Support. "The goal of performing analytics is to gain a better understanding of what's happening with students so that we can determine how our university can serve them better."

Institutional forecasting

One of the key areas of focus for Archer and her team is supporting the school's enrollment plan, to help university decision makers forecast demand and formulate institutional policies. For such a large university, accurate enrollment planning is crucial.

"We look at population projections and high school graduation rates, within the state and across the nation, to determine a demand forecast for enrollment. Enrollment is a significant focus for us; predictive analysis ensures we stay on top of it," explains Archer. "We go back each year to assess the accuracy of our results – our model is accurate within 1 percent. We implement correction factors on a year-to-year basis to continually teach the model and improve the future accuracy."

Forecasting enrollment, says Archer, supports administrative planners across the entire university, who use the projections to establish budgets, determine what courses will be offered and how many faculty resources will be needed. The projections are also used by the state to determine aspects of funding the university receives.

According to Archer, students nationwide take longer to graduate, with a smaller percentage of students than in the past graduating within four years. Coupled with the part-time and transfer students, who typically earn degrees over a longer time frame, multiyear planning at a university can be a difficult exercise. Using SAS, Archer's team created a chart to demonstrate the 10-year effect of student enrollment.

"One of the models we developed looked at average student course loads over time," she explains. "It has allowed us to look at the implications of admitting one student, or an aggregate of freshmen or transfer students, over a 10-year period. There are still people enrolled up to year 10 – not many, but we can understand the long-term effects of the admissions decision we make today."

I couldn't imagine doing my job without SAS. I don't know how we would be able to manipulate some of the vast and complex data sources that we use.
University of Central Florida Sandra Archer

Sandra Archer, PhD
Director

Institutional aspirations

In the area of strategic planning, UCF performs benchmarking analysis against two quantitatively generated lists: one that is made up of similar institutional peers and another comprising aspirational peers, or schools that UCF strives to emulate. The benchmarks rely on data from the National Center for Educational Statistics and are used by a wide variety of university staff. For example, UCF uses the data to look at how other institutions are organized, student characteristics, student-to-faculty ratios, salary equity and student retention and graduation rates.

"Eight years ago we developed a set of benchmarking peers and held the list static over time," explains Archer. "Recently, we used a new clustering analysis technique to rework the lists, and we looked back to see how the institution had changed compared to the two original lists. Some of the institutions that we once considered aspirational had become comparison peers."

The impact of student life cycles

In the area of management analysis, the Office of University Analysis and Planning Support provides university leadership with insight on student behavior while at school and after they graduate.

"We don’t just look at students as they come in the door, we look at students over the course of their entire life cycle here, as well as post-graduation success factors,” says Archer. “We look at what their majors are at graduation and compare that to their majors at the time of registration. We can see how many times they’ve changed majors, and how many graduated with a typical number of credit hours. With data from the National Student Clearinghouse’s StudentTracker Data System, we’ve launched a project that helps us track students after graduation. For instance, this allows us to see whether they enrolled elsewhere after UCF. I also can tap into data from the state of Florida to determine how many UCF graduates find employment within the state. It gives the university a clearer picture of how we are preparing students for life after UCF.”

Archer's team also applies a percentage of its time to exploratory analysis, which provides insight in support of forward-looking decisions.

"We keep an ear to the ground about what is happening now, and what is on the horizon," she says. "We also look at what other institutions are doing to determine if there is something we might want or need to implement. SAS gives us the ability to perform a lot of analytics quickly, which allows more time to pause and look at the bigger picture. It also helps us find patterns in the data that we might not otherwise have noticed.

"I couldn't imagine doing my job without SAS," concludes Archer. "I don't know how we would be able to manipulate some of the vast and complex data sources we use. The more we do, the more people see what we can do. There has been an increase in demand, especially from special programs and our regional campus system. We can take an overall university analysis and apply the technique to a small area of the university, to provide the answers they need. SAS makes it very easy for us to do."

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Challenge

UCF required a reporting and analysis system for enrollment, strategic planning, management and exploratory analysis to provide university leaders with insight that supports proactive decision making.

Solution

SAS® Business Analytics

Benefits

The university has a better understanding of student life cycles and uses accurate enrollment forecasts to improve operational planning, optimize resource allocation and support budget decisions.

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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