Six ways to think about risk and student success: different mindsets can influence the approach an institution takes in promoting student success.
The ways in which stakeholders across an institution--administrators, faculty and staff members-- think about student success will dramatically influence how they program, intervene and ultimately structure efforts to identify at-risk students and to help them succeed. These mental models frame an individual's thought process relating to a particular concept and can greatly impact student success plans--in other words, influencing what we focus on and why. In this web seminar, an expert researcher in student success and retention discussed the six mental models that are commonly used by institutions, administrators, faculty and staff to think about risk and student success; outlined the strengths and weaknesses of each; and described how to identify which mental models are useful for an institution.
A mental model is a set of thoughts and assumptions you have about how something works. In many cases, these are high-level conceptualizations that we may not discuss or realize we have. But these models greatly influence our expectations, decision-making and interpretation of outcomes. In the case of student risk and success, the mental models we have influence how we identify and intervene with students who need help. As a result, they drive our solutions when trying to change student outcomes.
When examining student risk, there are six common mental models used on campuses. It is important to note that there is no best model. Each model has strengths and weaknesses, and can be effective in helping at-risk students. Instead of using a single model, most institutions benefit from a systematic approach using multiple models.
Whether it's the student who bounces back from an impossible situation or the student whose departure was so painful that you remember it years later, the stories of our students have real power. Stories engage audiences and share vivid images. Whether positive or negative, stories leave a lasting impression, so it's not surprising that a story has more impact than any data point.
The strength of the story is its sticking power. The research is clear that emotions tend to be memorable. And stories that tap emotions can be memorable. Because of that attachment, it can help motivate us to change. However, a story is not a useful approach if the, story is unusual, does not match the experiences of many students, or focuses on the wrong issues. A story that is a one-time event may point us to solutions that will have little to no impact on our students.
Knock on the Door
This model is about who shows up, and it is very reactive. The Knock on the Door model focuses on who asks for help and who comes to our offices. These are the students who show up on the radar because they choose to. As an institution or as a professional, you have to be responsive to who knocks on the door and serve the needs of students as they arise.
There are two primary issues with this model: Students may never come, or they may come when it's too late. An institution can build a comprehensive network of services, but some students may never use those services. For other students, if we knew that they were experiencing issues two or three weeks earlier, we might have been able to help them. So this approach does have some limitations.
From financial holds preventing registration to not signing a residence hall contract for the following year, triggers--or flares--exist on any campus. Triggers send an indicator that a student may need assistance. By their nature, triggers are concrete and well-defined. When you see one, you know exactly what has prompted it and exactly what conversation to have with that student.
But the downside to the Trigger mental model is that it is reliant on the quality and quantity of the triggers. What happens if our triggers are not catching the students who need help the most? What happens if there are triggers we are missing? And what happens if we have too many triggers? If an institution has set up triggers that do not catch the right students, or if the triggers catch so many students that it creates an unreasonable workload for staff, it can defeat the purpose of putting triggers in place.
When looking to move beyond stories and triggers, a common area to examine is the population of students who leave. This mental model leads to the creation of a profile of who leaves the institution. For instance, if you do a survey of withdrawing students, that data can serve as a mechanism to help you figure out who is leaving. This approach is interesting, particularly if you discover information that was not on your radar. It is also the first mental model we've discussed that uses data to make sense of risk.
One dilemma with this approach is that we gather data about those who leave, but then target our programs for a broader audience than just leavers. At the start of the semester, there is no way to know if a particular student is going to leave or stay. Therefore, any intervention is targeted at the entire group of students who share the characteristic. This approach can also be problematic if that characteristic is not just a leaver characteristic, but is also present in the whole student body.
A natural evolution beyond profiling students who leave is to take a comparative approach. In this mental model, an institution will compare the students who leave to the students who stay and use statistical analysis to highlight the differences between the two groups. This approach to student success can have real power because it not only uses data, but also begins to look at student success in a broader perspective.
However, the downside to this model comes with the patterns we find and using those patterns to create a picture of who is at risk. While this may seem simple to do on the surface, in reality it is far more complex. A comparison may tell you, for instance, that students with low high school grades are more likely to leave the institution than other students. But if the overall number of students with low high school grades is small, then it does not help us understand the breadth of the overall leavers population.
Another option for using data to influence our student success efforts is predictive analytics. In this mental model, data is used for more than creating a simple description. Instead of examining what happened or why, predictive analytics uses data to predict what will happen in the future. The predictive analytics approach works for those who try to make sense of student risk in a statistical way. This mental model uses a significant amount of information in a smart and efficient manner.
There are certainly limitations to this approach. First, predictive analytics is complicated when you think about the range of data that could be included. How do you account for or deal with missing data in models, particularly when it's missing for a reason? A predictive analytics approach can also focus too much attention on perfecting the model and not enough time on the interventions. For this model to impact student success, we need to spend significant time discussing what will be done with the predictions. The perfect model has no impact if no one uses it.
Ultimately, there is no correct mental model to use. Most institutions and departments use multiple mental models, but do so implicitly without clearly fitting models to situations. An understanding of when and how these mental models are used can bring a fresh perspective to our student success efforts, improve our conversations, and foster new collaborations that help us identify and support at-risk students.
SHERRY WOOSLEY, PH.D.
Director of Analytics and Research Skyfactor
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|Title Annotation:||SPONSORED WEB SEMINAR DIGEST|
|Date:||Jun 1, 2016|
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