Month: September 2026

As a VP of Student Affairs, your success is increasingly measured by a single, high-stakes metric: student retention. In an era of shifting demographics and intensifying competition, the pressure to not only enroll but also graduate students has reached a fever pitch.

For years, student success initiatives have relied on the dedication of advisors and the intuition of faculty. However, these traditional methods are frequently proving insufficient. By the time a student’s struggle is visible to the naked eye, the window for effective intervention has often already closed. At Datatelligent, we believe the solution lies in a fundamental shift in perspective. To meet modern retention goals, institutions must transition from a reactive posture to a data-driven, predictive model of student support.

The Problem with the Status Quo

The current reality on many campuses is one of “lagging indicators.” Institutions typically wait for mid-term grades or faculty alerts to trigger outreach. While these are valuable touchpoints, they represent late-stage evidence of a problem that likely began weeks or months earlier.

Waiting for a student to fail a mid-term exam is a high-risk strategy. By that point, the student may have already disengaged from campus life, fallen hopelessly behind in their coursework, or decided that the financial burden is no longer worth the struggle. Early intervention is the cornerstone of retention, yet early intervention is impossible if you are only looking at outcomes that occur halfway through a semester.

Building an At-Risk Student Identification System

To intervene early, you need to see the “digital breadcrumbs” students leave behind long before a grade is ever posted. This requires breaking down the silos between departments and connecting disparate data sources.

A robust at-risk student identification system integrates data from the Learning Management System (LMS), Student Information System (SIS), and even non-academic sources like dining hall card swipes, housing logs, and gym attendance. When these data points are viewed holistically, they tell a story. A sudden drop in LMS logins combined with a decrease in card swipe activity at the student union isn’t just a coincidence; it is a behavioral pattern that signals a student is withdrawing from the campus community. By centralizing this data, institutions can identify vulnerable students based on engagement, not just performance.

Leveraging Predictive Analytics for Student Retention

Identifying “at-risk” students is only the first step. The next evolution involves moving beyond simple alerts to sophisticated modeling. This is where predictive analytics for student retention becomes a game-changer.

By employing machine learning models, institutions can analyze historical data to identify the subtle precursors to attrition. These models can weigh dozens of variables—from financial aid status and first-generation background to high school GPA and real-time behavioral data—to assign a “success score” to every student. This allows your team to prioritize outreach to those who are statistically most likely to drop out, even if they currently have a passing grade. Predictive modeling transforms your staff from a reactive “firefighting” crew into a proactive success team that can prevent the fire from starting in the first place.

Choosing the Right Student Retention Analytics Software

Not all platforms are created equal. When evaluating student retention analytics software, VPs of Student Affairs should prioritize three critical areas:

• Seamless Integration: The software must be able to ingest data from your existing tech stack (Banner, Colleague, Canvas, etc.) without requiring a massive overhaul of your IT infrastructure.
• Real-Time Insights: Data that is a week old is already stale. You need a platform that provides live dashboards, allowing advisors to act the moment a student’s behavior deviates from their baseline.
• Usability for Frontline Staff: The most powerful data in the world is useless if it isn’t accessible. The software should provide intuitive, actionable insights that advisors can understand at a glance, enabling them to focus on the human element of student support rather than data interpretation.

Conclusion

The mandate for Student Affairs has changed. It is no longer enough to provide excellent services and hope students find them; we must use the data at our fingertips to find the students who need us most. By moving toward a proactive, predictive model, institutions can significantly improve student outcomes and institutional stability.

Learn how Datatelligent empowers VP of Student Affairs to transform their retention strategies by turning complex data into a clear roadmap for student success.

A strong sense of belonging is having a major moment in higher education, and for good reason. While the feeling that you belong has always been crucial in personal relationships and work, researchers emphasize that defining and measuring it in educational settings remains elusive. 

Understanding how students find their place—especially leading up to and during their first college experience—is critical for boosting student retention and enrollment success. 

Why a Sense of Belonging Matters for Student Success 

Social connection is vitally important for mental health. In fact, Psychology Today (2025) reports that it is one of the “single strongest contributors to one’s happiness and well-being”. Isolation and depression—feelings counter to belonging—can begin as early as elementary school. Research shows that children with high levels of belonging see themselves as important and likable, which drives their willingness to participate. The confidence that belonging brings also builds social capital, influencing how others perceive them. 

However, research demonstrates that specific student subgroups are at a higher risk for non-enrollment and dropping out because they may not believe they belong. When historically under-served students, first-generation students, and students with intersectional identities (such as race, ethnicity, age, religion, socioeconomic status, or sexual orientation) face discomfort and isolation, the potential to impede their success is much higher—especially when compounded by financial stress. 

As Dias-Broens et al. note, “students’ sense of belonging was found to be positively related to emotional wellbeing, academic motivation and success in higher education”. 

The Challenge of Measuring Student Belonging 

If we understand its relationship to college persistence, why is measuring a student’s sense of belonging so difficult? Here are the primary hurdles: 

• Unique Campus Factors: Sense of belonging cannot be studied in isolation. Like all social science research, it is influenced by a unique set of variables specific to each individual campus. 

• Subjectivity in Self-Reported Data: Most analyses rely on self-reported data. While this yields important qualitative information, it depends heavily on students understanding the survey questions and accurately assessing their own feelings of acceptance. Administering emotional intelligence assessments in First-Year Experience (FYE) classes reveals that answering from the viewpoint of who you truly are versus who you want others to think you are is incredibly difficult for new college students. 

• Declining Survey Response Rates: It is becoming harder to convince students to share their private thoughts, a phenomenon directly correlated to the engagement we are trying to measure. According to the National Survey of Student Engagement (NSSE), response rates have plummeted from 38% in 2010 to just 23% in 2024. Similar declines have been observed in institutional effectiveness offices, with the Ruffalo Noel Levitz Student Satisfaction Inventory (SSI) hitting a new low of under 30%. 

A Mixed-Methodology Approach to Measuring Campus Engagement 

So, what can enrollment managers and higher education administrators do to measure this vital influence on persistence and retention? The most effective option is to use a mixed-methodology approach: 

1. Direct Measures of Engagement 

Track behavioral data to see how students are interacting with the campus ecosystem. Key metrics include: 

• Attendance in physical and virtual classroom environments. 

• Academic participation (e.g., LMS discussion boards and classroom discussions). 

• Facility usage, including tutoring centers, libraries, and dining halls. 

• Time to first enrollment and re-enrollment rates. 

• Event engagement, such as athletics, fine arts, and student clubs or organizations. 

2. Indirect Measures and Standardized Surveys 

Use established national surveys to provide insights on engagement and performance gaps. Valuable tools include: 

• National Survey of Student Engagement (NSSE) 

• Community College Survey of Student Engagement (CCSSE) 

• Student Satisfaction Surveys from Ruffalo Noel Levitz 

3. Early Mental Health Assessments 

Mental health surveys are highly useful tools to determine if a significant proportion of first-time students feel isolated. Administering these surveys early—ideally during the first 8 weeks of the fall semester—provides a critical window for intervention before a student decides to drop out mid-year. 

Ultimately, measuring belonging is only step one; the most important thing is to take action using the data you have. 

Are you measuring sense of belonging in relation to persistence and retention? Datatelligent can support your efforts to improve retention through mixed methods research with data integration in a secure cloud-space for traditional analytics and AI. Ask about our Fusion platform today! 

References & Further Reading 

• Bono, T. (2025, February 26). Sense of Belonging in College: Hard to Find, Hard to Measure. Psychology Today. 

• Dias-Broens, A. S., Meeuwisse, M., & Severiens, S. E. (2024). The definition and measurement of sense of belonging in higher education: A systematic literature review with a special focus on students’ ethnicity and generation status in higher education. Educational Research Review, 45. 

• The Chronicle of Higher Education. (2026). Read about Dr. John B. King, Jr.’s focus as the Chancellor of the State University of New York in The Chancellor of Belonging, featured in People to Watch 2026. 

• Lam, V.W.H., & Webb, C. (2026). Graduate Student Center: An Increased Sense of Belonging for Graduate and Professional. New Directions for Higher Education. 

• Stickl Haugen, J., Frawley, C., & Bledsoe, K. G. (2025). The Development of the Student Belonging Scale: A Measure of School Belonging. Professional School Counseling, 9, 1-12. 

• Vincente, S., & Matusovich, H. (2026). Sense of Belonging and Intent to Return in a STEM Internship Program. New Directions for Higher Education.