Academic advisors work very hard to get students the help they need at the time they need it, often with little information about the nature of an individual’s problem.
But useful information does exist; in fact, universities are awash in student data, and many have undertaken detailed analyses to determine the factors most strongly linked to student success. That information, however, is rarely converted into actionable strategy or integrated into existing student support workflows.
The inability to use that data to intervene with students at risk for attrition has led most institutions to two suboptimal extremes. Either they rely on a “one-size-fits-all” standard that treats all students the same (wasting resources on those who don’t need it and underserving others with acute issues), or they devote the majority of advising investment to one or two segments thought to be at increased risk, such as first-term students or athletes.
Most advising models misallocate scarce time
To find the middle ground between these extremes, Eastern Connecticut State University (ECSU) redesigned their advisor intervention model around predictive analytics. By isolating factors linked closely to both academic failure and attrition (and including a broad set of variables such as FAFSA school preference, zip code, declared major, and first-generation status), advising leaders identified four subpopulations in need of distinct kinds of support:
1. Low risk: Few factors associated with academic or engagement problems; students are monitored for changes in performance
2. Engagement risk: Academically prepared, but likely to transfer to an institution closer to home or deemed a better fit; students are connected with faculty mentors and major-specific resources
3. Academic risk: Likely to be engaged, but struggle in coursework; students are provided with tutoring services and supplementary instruction
4. Compound risk: Several engagement and academic risk factors; students are given intensive success coaching by advisors and tracked throughout the semester
Tailoring interventions based on risk
Each year, incoming freshmen are placed into one of these “targeted advising cohorts” based on historically predictive patterns in ECSU’s analyses, allowing advisors to jumpstart the appropriate interventions much earlier than before. As a result, fall-to-spring student retention has increased for each group by an average of 1.6 percentage points.
Adjusting student risk over time
Even the most sophisticated predictive risk model will err some of the time. Advisors should use students’ ongoing degree progress and behavior cues (e.g. gaps in attendance, campus involvement, or transcript requests) to regularly adjust individual risk profiles.
For example, ECSU found that attending library orientation upon matriculation, which is tracked through their student information system, is a reliable proxy for “grit.” This behavior analysis can give advisors better insight into students’ likelihood to persist, and a more robust method of allocating precious time and resources to those in greatest need.
To learn more about leveraging advisors to improve student persistence, read our study, A Student-Centered Approach to Advising.