In their effort to improve outcomes, colleges and universities are becoming more sophisticated in how they analyze student data – a promising development. But too often they focus their analytics muscle on predicting which students will fail, and then allocate all of their support resources to those students.
That’s a mistake. Colleges should instead broaden their approach to determine which support services will work best with particular groups of students. In other words, they should go beyond predicting failure to predicting which actions are most likely to lead to success.
Higher education institutions are awash in the resources needed for sophisticated analysis of student success issues. They have talented research professionals, mountains of data and robust methodologies and tools. Unfortunately, most resourced-constrained institutional research (IR) departments are focused on supporting accreditation and external reporting requirements.
Some institutions have started turning their analytics resources inward to address operational and student performance issues, but the question remains: Are they asking the right questions?
Colleges spend hundreds of millions of dollars on services designed to enhance student success. When making allocation decisions, the typical approach is to identify the 20 to 30 percent of students who are most “at risk” of dropping out and throw as many support resources at them as possible. This approach involves a number of troubling assumptions:
- The most “at risk” students are the most likely to be affected by a particular form of support.
- Every form of support has a positive impact on every “at risk” student.
- Students outside this group do not require or deserve support.
What we have found over 14 years working with students and institutions across the country is that:
- There are students whose success you can positively affect at every point along the risk distribution.
- Different forms of support impact different students in different ways.
- The ideal allocation of support resources varies by institution (or more to the point, by the students and situations within the institution).
Another problem with a risk-focused approach is that when students are labeled “at risk” and support resources directed to them on that basis, asking for or accepting help becomes seen as a sign of weakness. When tailored support is provided to all students, even the most disadvantaged are better-off. The difference is a mindset of “success creation” versus “failure prevention.” Colleges must provide support without stigma.
To better understand impact analysis, consider Eric Siegel’s book Predictive Analytics. In it, he talks about the Obama 2012 campaign’s use of microtargeting to cost-effectively identify groups of swing voters who could be moved to vote for Obama by a specific outreach technique (or intervention), such as piece of direct mail or a knock on their door -- the “persuadable” voters. The approach involved assessing what proportion of people in a particular group (e.g., high-income suburban moms with certain behavioral characteristics) was most likely to:
- vote for Obama if they received the intervention (positive impact subgroup)
- vote for Obama or Romney irrespective of the intervention (no impact subgroup)
- vote for Romney if they received the intervention (negative impact subgroup)
The campaign then leveraged this analysis to focus that particular intervention on the first subgroup.
This same technique can be applied in higher education by identifying which students are most likely to respond favorably to a particular form of support, which will be unmoved by it and which will be negatively impacted and dropout.
Of course, impact modeling is much more difficult than risk modeling. Nonetheless, if our goal is to get more students to graduate, it’s where we need to focus analytics efforts.
The biggest challenge with this analysis is that it requires large, controlled studies involving multiple forms of intervention. The need for large controlled studies is one of the key reasons why institutional researchers focus on risk modeling. It is easy to track which students completed their programs and which did not. So, as long as the characteristics of incoming students aren’t changing much, risk modeling is rather simple.
However, once you’ve assessed a student’s risk, you’re still left trying to answer the question, “Now what do I do about it?” This is why impact modeling is so essential. It gives researchers and institutions guidance on allocating the resources that are appropriate for each student.
There is tremendous analytical capacity in higher education, but we are currently directing it toward the wrong goal. While it’s wonderful to know which students are most likely to struggle in college, it is more important to know what we can do to help more students succeed.