Seven Data-Driven Ways to Identify At-Risk Students

As students return to campus and settle in for the new semester, administrators must face the daunting task of unpacking a complex and all-important problem: retention. The stakes are high in those first few weeks, as the excitement subsides and the reality of college life sets in. Given this scenario, it’s easy to see the benefits of implementing an early alert system. The key is to quickly identify students who may be at-risk, and then shepherd them to the resources they need to be successful. While a hyper-focus on individual learners is important, it’s crucial to also consider what patterns and characteristics are shared by groups of students to get a true picture of the state of retention on campus.

Here are seven ways to tap into new data sets for better insight and proactive outreach:

  1. Midterm Grades and Attendance: Faculty and staff who work closely with students should start utilizing early alert tools in the first few days of the academic year. Providing information to a student’s support network is crucial in getting students the resources they need. However, the beginning of the term is not the only critical period. Looking at aggregated data sets related to grades and attendance at the midterm benchmark can also help identify at-risk subpopulations. Students who are failing courses and missing classes at midterm are clearly in academic trouble. What’s more, the impact of low grades at this point in the semester isn’t just practical–it can also influence students’ feelings about college success in general.

  2. Resiliency, Determination, and Commitment: It’s important to identify noncognitive or psycho-social attitudes and behaviors which might influence how students react to grades. For example, you can have two students who do very poorly on their first chemistry exam. One student might say, “This means I’m not going to get into medical school and I’m not smart enough for college,” and just give up. The other might conclude, “I didn’t do very well on this exam, but now I know what a college chemistry exam is like and I know that I need to change my approach. I need to visit the Student Success Center and visit my professor during office hours.” The first type of student reflects a group that is vulnerable, while the second typifies learners who demonstrate resiliency. Formally assessing non-cognitive factors early in the semester can help predict how resilient students will be when adjusting to the demands of a rigorous educational environment.

  3. Course Rigor and Learning Outcomes Alignment: Are the learning outcomes for specific courses aligned with the rigor that’s expected at their corresponding levels? If students are taking introductory courses with outcomes written for a deeper level of learning than they’re ready for, they may struggle to keep up. At the same time, seniors in 400-level courses with low-level learning outcomes may become bored. An aggregate analysis of course-level learning outcomes can ensure your curriculum spans Bloom’s taxonomy and appropriately scaffolds the learning. This kind of analysis may flag an entire course, cohort, or department for having students at risk of falling behind academically.

  4. Support Service Availability and Usage Metrics: We know that students frequent campus support resources such as the wellness center, tutoring center, or disability services office. When they take a seat in a waiting room–or leave before they’re seen–they aren’t just students seeking services. They also become data points for predicting retention. Tracking location check-ins and checkouts can yield valuable information about whether services are being used, what they’re being used for, and why students might leave before being seen. By collecting some personal information, campus administrators can follow up with students regarding their experience. You might also flag underutilized resources, pinpoint the appropriate audience for those services, and consider creative approaches to outreach. This level of targeting can be particularly effective for nontraditional students.

  5. Student Engagement Levels: First-year residential students are likely to be highly engaged in the first four weeks of classes, when many residence halls condense their programming to maximize student involvement. But later in the semester, the sense of engagement broadens to include the campus as a whole. Connections to the residence hall community may weaken as a result. Tracking the availability and timing of residence hall offerings, as well as student attendance, can help identify cohorts who may be lacking a sense of community. Commuter students, for example, have unique needs and are often at a higher risk of feeling disconnected from the campus community. Consider data related to the swath of events, organizations, and programming designed specifically for commuters. What’s available to that student group? Is there a student organization for commuters, and if so, how does membership compare to the size of the group as a whole? To further target their needs, consider doing formative assessment. Quick surveys administered on tablets near a popular commuter parking area, for example, can be an effective way to gather feedback.

  6. Financial Stress Indicators: According to the 2015 NSSE report, financial stress remains a significant barrier for student success. The findings showed that “[d]espite their busier schedules, financially stressed students appear to be engaged in both academic and co-curricular activities on par with their peers, and more so in some respects. However, their lower ratings of interactions with others and environmental support are cause for concern.” Students experiencing financial strain can be identified by early alert indicators such as: incomplete FAFSA documentation; confirmation that they were not offered a financial aid package to cover tuition, fees, room, and board; and evidence showing they did not purchase required course materials by the end of the first add/drop period.

  7. Data Connections: Individually, none of these trends may raise a serious flag about a student or groups of students, but taken together, they become highly informative data points. Imagine a bright student who scores low on an accounting exam. This single event may not be cause for concern, but what if she demonstrates low resiliency? After a strong start to the semester, her grades in another course begin falling, she stops going to the tutoring center, and she drops out of a student organization. By looking at these data points collectively, institutions can gain much greater insight into individual students and groups of students who may be most at risk and can now adjust programming and resources accordingly.

To maximize student success and retention, higher education administrators should consider individual as well as group trends. Are there patterns associated with lower retention? What characteristics do at-risk students seem to share? Are there connections that we can make using information from seemingly unrelated aspects of the campus experience? A deeper dive into institutional data is a good first step toward actionable insights.


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Emily-Rose Barry

Director, Campus Success | Campus Labs

Emily-Rose Barry is Director, Campus Success, for Member Campuses in our south region. She provides high-level consulting on the value of campus-wide, data-oriented process improvements for institutions.