Predictive Analytics In Higher Education: Enhancing Enrollment

Boosting Student Enrollment Yield With Data-Driven Decision Making

Predictive analytics have been used in various industries, including finance, healthcare, and manufacturing. However, they also play a major part in higher education. Colleges and universities can use predictive analytics for numerous purposes, including adaptive learning and data-driven decision making. One of the most impactful applications, though, is to enhance student enrollment. This article will examine the role of predictive analytics in higher education, how administrators can leverage it to boost student enrollment, and what factors might hinder the successful implementation of predictive analytics.

What Is Predictive Analytics In Higher Education?

Predictive analytics uses historical data, statistical algorithms, and Machine Learning to project what may happen or what a student might do in the future. For example, predictive analytics can help higher education institutions make financial aid decisions. Institutions might use Machine Learning models to analyze past data on financial aid packages to determine the probability that a student will enroll once given a specific aid amount.

Using predictive analytics enables institutions to draw actionable insights from an enormous amount of data. This helps colleges and universities make more informed decisions about student recruitment, enrollment, and retention.

What’s The Difference Between Predictive And Prescriptive Analytics?

Predictive and prescriptive analytics are both used to support institutional decision making, optimize admissions, and improve enrollment. However, there are key differences between the two. While predictive analytics predicts what may happen in the future, prescriptive AI recommends or prescribes actions an institution can take to achieve a desired outcome.

For instance, a predictive model might examine enrollment trends and graduation requirements to forecast a rise in demand for a particular course. A prescriptive model, meanwhile, would take this a step further by suggesting that institutions offer additional course sections. This would ensure students can take the classes they need to graduate on time.

How Can Predictive Analytics Enhance Student Enrollment?

Here are four examples of how predictive analytics can boost student enrollment at higher education institutions:

1. Optimizing Recruitment Strategies

Institutions can gather data from students during their college search, including their geographical location, high school type, demonstrated interest, standardized test scores, and academic interests. Enrollment officers can use this information to tailor their recruitment efforts and conduct more targeted, effective outreach.

For example, if an institution’s predictive model finds financial concerns are a barrier to campus visits for out-of-state applicants, then enrollment managers could address this barrier by offering travel stipends to those prospects.

2. Boosting Conversions

By identifying the most effective recruitment strategies for different types of students, predictive models can increase enrollment yield. This was the case for a private, mid-sized university that tapped an AI company to help increase the number of students who matriculated. The company used predictive and prescriptive AI to target a subset of applicants likely to respond to phone calls from faculty and then advised the university to make personal calls. Preliminary results showed a 15% increase in the university’s enrollment yield.

3. Strengthening Retention Rates

Predictive models can analyze data like academic performance and attendance records to identify students at risk of dropping out. Colleges and universities can then respond by offering support services, keeping students in school, and ensuring their success.

4. Tackling Enrollment Challenges

A variety of factors can influence a student’s transition to college, including socioeconomic background, first-generation status, and attending a college in another state or country. Leveraging predictive analytics will help colleges and universities identify incoming students who may struggle to adjust to college life.

With this information, enrollment teams will know to invest in and provide students with effective resources like summer bridge programs and specialized advisors. This kind of support will reduce summer melt, strengthen retention, and guide students to graduation.

3 Barriers To The Successful Implementation Of Predictive Analytics

Though predictive analytics offer numerous advantages, distinct barriers prevent the implementation of predictive AI in higher education. Below are three examples:

1. Knowledge Gaps

According to a Liaison survey, although predictive and prescriptive AI do the most out of other AI types to improve admissions and enrollment, only about 40% of administrators use predictive AI for those purposes. And just 20% use prescriptive AI for the same tasks.

These survey results indicate a knowledge gap among higher education leaders. One potential solution for overcoming this gap is for administrators and other stakeholders to attend trainings and professional development sessions to learn more about the benefits of predictive analytics.

2. Algorithmic Bias

Studies have revealed evidence of algorithmic bias in AIED (Artificial Intelligence in Education) systems and other educational technology. A 2024 research article also found that Machine Learning models are less accurate at predicting success for racially minoritized students.

Such findings leave faculty and staff with valid concerns about equity, inclusion, and fairness, discouraging them from harnessing the power of predictive analytics. That’s why institutions should work with reliable partners to minimize algorithmic bias and other flaws in AI systems.

3. Privacy And Data Security Concerns

Ellucian’s 2024 “AI in Higher Education” survey of administrators found that 59% of respondents worry about data security and privacy.

Students are similarly concerned. The Future of Privacy Forum’s 2021 report on the privacy preferences and behaviors of students revealed that students care deeply about protecting their academic, professional, and personal information.

Facing potential objections from their colleagues and students, higher education leaders may choose to avoid predictive analytics. Instead of missing out on critical opportunities to improve enrollment, however, leaders should honor stakeholders’ concerns by prioritizing data privacy when choosing analytics software solutions.

6 Tips For Leveraging Predictive Analytics To Improve Enrollment

Now that we understand the barriers to implementation and possible solutions, we can proceed to leverage predictive analytics in higher education. Here are six tips to help administrators do so:

1. Begin With Clear Goals

Having clearly defined goals will help institutions decide on a strategy so they can use predictive analytics effectively and intentionally. For example, colleges and universities may want to learn about students’ behavioral patterns. Or, they could be interested in improving retention rates. Either way, clear objectives will allow institutions to work toward a specific outcome. They’ll also be able to choose appropriate methods, partners, and software to achieve those objectives.

2. Ensure Data Readiness

Data readiness is a state where an institution’s data is accurate, timely, complete, and suitable for decision making and operations. When an institution has high-quality, well-prepared data, leaders can draw insights from information and effectively act on those insights.

A higher education technology company offers a checklist for higher education leaders to ensure data readiness. For example, the company recommends administrators to:

1. Assign team roles and responsibilities to manage various tasks across different project stages.
2. Ensure rich data sources, including demographic information, academic history, behaviors, and levels of engagement.
3. Have at least two years of historical data to allow algorithms to make more accurate predictions.
4. Establish a post-launch action plan to ensure ongoing data validation and implementation of insights into decision making.

3. Guarantee Data Privacy And Security

Institutions must avoid compromising the data of students, faculty, and staff. They can do this by creating policies on data ownership and access. For example, policies can specify that enrollment managers only use predictive analytics for recruiting purposes. Or, that faculty members can only access the amount of student data necessary for timely interventions.

Colleges and universities should also inform students, faculty, and staff about how their data is being collected, used, and stored—and for how long. If institutions plan to use sensitive information like health records, they should obtain consent from these individuals.

4. Mitigate Bias

Machine Learning models can discriminate against students from disadvantaged groups, hurting institutions’ diversity efforts. Additionally, commonly used factors like demonstrated interest can undermine these students’ chances of admission.

For example, low-income students may not be able to afford to visit a college’s campus. Machines will interpret this as the students being less interested, even though this may not be the case. As a result, enrollment managers might overlook these prospects.

To reduce the risk of bias and its impact on enrollment, institutions should use a variety of data sources. They should also choose partners who prioritize equitable and inclusive algorithm design.

5. Work With Trusted Partners 

Colleges and universities must partner with companies who understand how to leverage predictive AI to enhance enrollment and achieve other institutional goals. They should also look at potential partners’ track records to ensure they’re capable of accomplishing these objectives.

Additionally, institutions should make sure partners emphasize algorithmic transparency, data privacy and security, and data monitoring. This will mitigate bias and promote data quality. As a result, higher education leaders will be able to make decisions that support enrollment yield.

6. Monitor Results And Strive For Continuous Improvement

To determine and improve the effectiveness of predictive analytics, administrators must review results regularly. Specifically, they should pay close attention to whether they’ve seen improvements in key metrics like total enrollment, conversion rates, and retention rates.

From there, higher education leaders can make adjustments—whether that’s redefining their goals or choosing a different analytics software solution. Advancements in predictive AI will continue at breakneck speed, however. So, leaders must monitor these developments and consider how they affect institutional efforts to improve enrollment.

Predictive Analytics Case Study: Florida International University 

To better understand the value of predictive analytics in higher education, let’s consider a real-life example. According to EdTech Magazine, Florida International University (FIU)—a public university in Miami, Florida—decided in 2014 to invest in analytics software after the Florida Board of Governors implemented changes to its system for funding higher education institutions. The new guidelines placed more pressure on state colleges and universities to promote student success.

As Hiselgis Perez, FIU’s associate vice president for analysis and information management, told the magazine, the university had to aggregate and analyze a large volume of student data to satisfy the guidelines. However, the data proved too “unwieldy and disparate to be actionable.” This meant leaders at FIU couldn’t predict which students were at risk of failing or dropping out.

Resolving to be more “predictive instead of reactive,” administrators took action by investing in analytics software and conducting trainings. The investment paid off. EdTech Magazine reported that FIU saw a 10% increase in their four-year graduation rates.

“We can slice data in ways that help us determine which interventions are needed based on risk factors for individual student groups,” Perez said of the analytics software. She and other FIU administrators have leveraged the data to make timely interventions, enhancing retention and ultimately allowing them to satisfy their and the state governing board’s goal of student success.

Conclusion

For institutions trying to combat enrollment climb, predictive analytics represents a streamlined, data-driven solution to recruiting and retaining students. Predictive analytics leverages historical data, statistical algorithms, and Machine Learning to project student actions and outcomes.

Higher education institutions can use the information provided to conduct more targeted outreach, reaching prospects who may have merely needed a personalized phone call or travel stipend to convert. Predictive analytics also help colleges and universities improve their retention rates and address enrollment barriers.

Though knowledge gaps, algorithmic bias, and privacy concerns can challenge successful implementation, administrators can overcome these obstacles by emphasizing training, fairness, and data security. From there, higher education leaders will be able to adopt predictive analytics to drive enrollment and provide the necessary support for student success.