Editor’s Note: This article is Part 3 of a three-part Student Affairs Executive series examining how colleges and universities are turning data into evidence of institutional impact. Part 1 explored the shift from documenting activity to demonstrating what changes because of student affairs’ work. Part 2 examined what it takes to build a division capable of doing this well, from assessment expertise and leadership strategy to data infrastructure and a culture of evidence. Part 3 explores the next step: using that evidence to intervene earlier, evaluate programs and make better decisions on behalf of students.
A missed registration window, declining course engagement or a financial hold can offer an opportunity to reach a student before a problem threatens their ability to continue. For student affairs leaders, the challenge is connecting those signals and deciding how to respond.
At the University of Kentucky (UK), connecting data across offices has helped student success professionals identify where support could make a difference and tailor their response.
“We invested heavily in our data analytic operation, our team, our infrastructure about a decade ago,” said Kirsten Turner, the vice president for Student Success at UK. “At the time it was one person, now it’s an entire office.”
That investment supports a broader effort to understand students’ experiences from the moment they consider attending through graduation. Internal handoffs should happen behind the scenes, Turner explained, rather than becoming something students must manage themselves.
“The student shouldn’t have to be navigating that space,” she said. “It should all just be seamless.”
Understanding What Students Need
About a decade ago, UK examined internal data and national research to develop its Student Success Conceptual Framework. The framework places students at the center of four interconnected areas: academics, belonging, financial stability, and mental and physical well-being.
Each area raises practical questions. Do students have the preparation and study skills to succeed in their courses? Do they feel connected and recognized on campus? Can they afford to continue beyond the first semester? Are they well enough to participate and learn?
These factors often intersect. A student working two jobs, for example, may struggle to reach the tutoring center, fall behind academically and become ill. Turner said UK has not encountered a student departure it could not connect to one or more of the four areas.
The framework also guides the Office of Student Success’s structure. Tutoring, advising and the career center fall within academics; financial aid, basic needs and financial wellness support financial stability. Student organizations, fraternity and sorority life, and residence life address belonging, while campus recreation, counseling and related services support well-being.
UK uses the same framework to organize its data and examine which programs and interventions students need.
Over the course of the effort, UK integrated approximately 50 systems of record into a shared data environment. Offices that had maintained separate records of appointments and participation could now contribute to a fuller picture of students’ experiences.
UK developed an in-house predictive model early in the process and has refined it as its data became more connected. It begins with approximately two to three dozen statistically significant variables, including first-generation status and whether a student lives more than 200 miles from home. The model estimates students’ likelihood of returning the following year and graduating within different time frames, including four or six years.
Staff can examine results for an individual student or groups such as a residence hall, an advisor’s caseload or a sorority pledge class. The model is updated as additional information becomes available, including midterm grades, academic and behavioral alerts, participation recorded through ID-card swipes, and responses to an entering first-year survey. UK trains it on the previous three years of data.
Student success professionals can access the information in real time to guide conversations and connect students with support. Subscores for the four framework areas help identify what Turner calls “opportunity flags.”
A student who appears well positioned to succeed but faces a financial obstacle, for example, may receive scholarship support tailored to that need. A student whose primary challenge is belonging may meet with an involvement officer to explore organizations and receive help taking the first step.
The information helps staff identify a specific obstacle and offer a response suited to the student’s circumstances.
Recognizing Earlier Signals
At Kansas State University (K-State), Bill Watts, the associate vice president for academic and career engagement, similarly emphasizes information that creates opportunities to act while students are still enrolled.
K-State tracks traditional measures such as fall-to-fall retention, graduation rates and term-to-term persistence. It also examines leading indicators, including registration holds, course withdrawals, advising engagement, orientation participation, student voice responses through EdSights, and whether students register during priority registration.
“What makes these data valuable is not the reporting itself but the action they enable,” said Watts.
One example is K-State’s campus-wide progress report process, through which faculty teaching undergraduate courses provide midterm feedback. During the most recent spring term Watts referenced, 89% of instructors participated, generating hundreds of alerts that academic advisors and coaches used for proactive outreach.
K-State embeds academic coaches within colleges and uses dashboards and student success technologies to help them identify students who may benefit from support. Its tools include EAB Navigate, EdSights, degree audit systems and Power BI dashboards, alongside its student information system.
The university is also working to bring more sources into a centralized data warehouse. Current analysis focuses largely on student information system and advising data, with plans to incorporate learning management system (LMS) engagement, campus involvement and card-swipe activity.
An emerging finding involves students whose LMS engagement is lower than that of peers in the same course section. Watts said those students may benefit from outreach. Comparing engagement within a section helps account for differences in how instructors use the platform.
“The most valuable insights often emerge when multiple indicators are examined together rather than relying on any single measure of risk,” said Watts.
Changing Programs and Institutional Practices
Beyond individual outreach, data can help leaders decide which initiatives to expand, adjust or discontinue.
At UK, Turner described a process for identifying an at-risk cohort, examining the drivers of that risk and estimating its retention likelihood. Staff can then simulate the expected result of an intervention, implement it and compare actual outcomes with expectations, including examining statistical significance.
That process has become part of how offices approach potential changes. Teams increasingly ask to run the model and examine the evidence before implementing an idea.
“We’ve got this culture of evidence now where people understand it, they understand the data, and they start making suggestions based on that data,” said Turner.
She said the university can discontinue initiatives that are not working and expand those showing gains. Building that capability, however, has taken approximately a decade of investment and refinement.
At K-State, direct student feedback helped support the expansion of EdSights. The initial implementation focused on first-year students. As students were transitioned off the service, some responded through the chat that they would miss the connections and wanted it to continue. Their feedback, alongside engagement with the platform, helped support investment in making it available to a wider range of students.
Watts also shared an example from an unnamed institution earlier in his career. Analysis showed that students owing modest balances below a predetermined threshold almost always resolved them. Yet different offices across campus were applying numerous registration holds.
The institution determined that the administrative burden and barriers outweighed the financial risk. It consolidated the processes into a single financial hold and increased the minimum balance required to trigger it. Watts said the changes significantly reduced registration barriers and improved priority registration participation.
“For me, the most important use of data is not producing reports,” said Watts. “It is using the data to change institutional behavior in ways that improve broader student outcomes and providing timely actionable opportunities for individual student interventions.”
Preserving Professional Judgment
As institutions explore AI and predictive analytics, Watts urges leaders to distinguish between estimating risk and identifying a concrete reason to connect with a student.
“Predictive models can inform outreach and help identify students who may benefit from support, but they should never become deterministic tools that replace professional judgment or meaningful human engagement,” he said.
Watts expects AI to make sophisticated analyses more accessible and help uncover actionable leading indicators. He also sees potential for it to reduce repetitive work in degree planning, enrollment navigation, degree auditing and routine communication, giving professionals more time for developmental advising, coaching and conversations about purpose, identity and career direction.
For institutions beginning this work, Watts recommends building a partnership among information technology, institutional research and student success leaders. He also advises starting with one leading indicator that frontline professionals consistently associate with students struggling or stopping out, then developing a strengths-based, validation-focused outreach strategy around it.
Turner’s experience underscores the sustained commitment that broader integration requires. UK’s leadership maintained its vision through difficult transitions, and developing the framework internally helped employees take ownership of it.
The next step can begin with a specific question: What information already available could help staff reach a student sooner or remove an obstacle the institution has created?
“Institutions often believe they need more data,” said Watts. “More often, they need to connect various data and develop a clear strategy for acting on the existing data.”
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With more than a decade of experience in B2B journalism, Rachel Chonko is Editor-in-Chief of Student Affairs Executive and Peake Media. She specializes in connecting leaders with the ideas, strategies and peers that can help them navigate complex challenges and strengthen their organizations.
Through Student Affairs Executive, Rachel works closely with senior student affairs leaders across higher education to explore the issues commanding executive attention — from student success, data and assessment to organizational strategy, financial leadership, technology and stakeholder engagement. Her reporting is grounded in conversations with the leaders doing the work, with an emphasis on practical insights, institutional case studies and peer perspectives.
Rachel also helps shape the Student Affairs Executive Summit, an intimate, discussion-driven event designed to bring senior student affairs leaders together for candid conversations, relationship building and shared problem-solving.
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