High student retention is a definitive indicator of student satisfaction and academic excellence—and it’s essential to a college or university’s financial stability. For many institutions, improving student retention is a higher priority than ever as nationwide enrollment declines. As of October 2023, the undergraduate dropout rate in the U.S. was an abysmal 32.9%.¹ Thankfully, improving student retention is something colleges and universities can control, and data is key.
According to a World Economic Forum survey², higher education students report the following reasons for dropping out of their programs:
- Take advantage of the current labor market
- Can't afford living expenses
- Can't afford tuition
- Have to financially support family
- Not sure what I want to study
- Poor academic performance
- College life different since pandemic
- Higher education isn't for me
- Mental/physical health issues
- I've learned enough to get a job
With proper support and resources from their university, students can often overcome these challenges. For example, a student may need assistance understanding and proceeding through the financial aid application process, they may need guidance from an academic advisor, or they may need additional university resources like tutoring. Most colleges and universities provide these resources and ensure they’re available and accessible for students.
The challenge is to utilize data to identify at-risk students and proactively intervene to provide them the support they need before they reach the point of dropping out. If colleges and universities can collect, integrate, and analyze their data in a strategic manner, they can reengage students in time to prevent dropout and improve retention rates.
Enhancing Outcomes and Academic Success: The Role of Student Data
Colleges and universities have a wealth of data at their fingertips, and this data can be leveraged to help students overcome challenges and succeed in their programs. This data can include information such as:
- Attendance
- Academic performance
- Professor feedback
- Demographic information
- Financial aid information
- LMS activity
- Employment status
- Parental status
- Scheduling
- Library visits
- Cafeteria visits
- Voluntary online chat participation
Some of these data points, such as attendance and academic performance, are clear indicators of student success. Others, such as cafeteria visits and voluntary online chat participation may not be as obvious but provide insight into student engagement and can factor into predictive analytics algorithms that identify at-risk students. If the analytics identify an at-risk student, the university can intervene and provide the student the support they need to retain and succeed in their program.
AI Integration: A Student Retention Game Changer
Combined with the data highlighted above, AI can empower universities to identify and proactively connect with students who are at-risk for dropping out. If a student is identified as at-risk, AI-enabled email, text, or chat can send the student immediate outreach to provide the resources they need or direct them toward the appropriate campus office for help. This enables advisors/counselors and faculty to work more proactively and efficiently so they can maximize their impact on student success and retention.
Universities are also using AI to improve student engagement within courses, which is proven to boost student retention.³ Course design and facilitation can benefit from the use of AI through:
- Personalized learning paths, allowing students to learn at their own pace, leading to increases in motivation.
- Individualized communication on the student’s preferred platform (text, email, social media).
- Augmented and Virtual Reality driven projects and classes that create immersive learning environments.
- Enhanced Online Learning which is optimized for individual users.
- Automated and personalized feedback in real time.
Implementing Data-Driven Student Retention Strategies with Everspring
Everspring’s student success support scans student behavioral data from LMS and SIS systems and uses a proprietary “early-warning” algorithm to identify students who may be at risk before they reach a high-stress moment that puts their persistence in jeopardy. Our algorithm triangulates course activity; assessment and grade performance; self-disclosed challenges and circumstances; and faculty feedback to make an overall assessment of support needs and retention risks. These students are then triaged by Everspring’s student success coordinators and the university to ensure they receive the support they need to retain in their program.
Contact Everspring to learn more about how we can empower your university’s data to move the student retention needle in your favor.
1. Education Data Initiative
2. World Economic Forum
3. Behavioral Sciences Journal