1. Probation is not actually a component in the model—Grades, number of D/F, and Cum GPA are.   The rationale for this is that probation status is a factor of semester GPA, not cum, and the probation status may be only one semester of struggles.  As advisors, we obviously want to outreach and connect with students who are on probation and we already prioritize that outreach. While it currently doesn’t take into consideration the probation status, it does consider in multiple ways the
  2. Several of the students have transfer / accumulated credit that positively impacts the model
      It’s important to recognize that the model is just a starting point for identifying students and that there are number of components in the model that impact the score—the intent with using the model is not to be an end-all/be-all thing.  It’s one tool among several that we can use in prioritizing caseloads.  And, there’s a comprehensive amount of data that goes into it.    


Another thing to also keep in mind is that our model approaches it from a “population health” perspective—regardless or how well or poorly the overall population is doing, the model will always identify 10% of our student population as high—20% as moderate—70% as low.  This give us the opportunity to identify the students from our overall student body who are most in need given our specific population.  The model does not base the rating on a score (i.e.: scores below X are high… moderate… low).  


If you have time and want to, take a look in the analytics tab on navigate, then in Population Health Dashboard, and you can see the macro-level percentages based on GPA where students land in the high, mod, low support levels.   Since we just refreshed the model last summer, we are doing a lot of analysis (working with IR and EAB) to determine how accurate we think it is and we have opportunities to update it and improve.  So, your feedback is really helpful and timely as we are right now diving into what the fall student cohort looked like with the model compared to our other data/info we know about students. We may want to reconsider if probation status is included, for example.   We are also working on trying to integrate more conversation, presentation, etc. into the network town halls so folks have a better understanding of how the system works and can, in turn, feel for confident in the data.