What determines if a student is at risk? How can this be measured? What is the meaning of “At Risk”?

These same questions are asked by school districts across the United States every day, but the answers can be very different depending on the district, state, student grade level and even based on the opinions of well-meaning educators. Many people assume that “at risk” indicates the likelihood that a student will drop out and not finish school. While this is a valid assumption, I think educators can look at this in a more flexible way.

I would suggest that an at-risk student is a student who is achieving well below their potential, in or out of the classroom.

By defining a student’s potential as not just their grades but their school life in general, students at all grade levels may be included in the definition so that risk mediation can be developed as early in their school life as possible.

There is no globally accepted formula for measuring risk beyond those mandated by some state Departments of Education, and those formulas are typically shallow and targeted to select students at risk of dropping out. So if your district decides to measure risk by the broad definition above, how can you collect and quantify risk data?

It is easy to determine high risk for students who are clearly struggling in key areas such as grades, discipline and attendance, but what about the student where these risk tendencies are more subtle? These are the students that with appropriate intervention can make the most significant improvements. Moreover, all students at risk deserve equal attention. The only way to have a real impact on student performance is to accurately measure that performance, so that appropriate and meaningful action can be taken.

Data analytics programs, such as the system I work with, Tyler Pulse, can take a very broad view of risk. When a student is evaluated for risk through a program like this, the system will of course take many of the usual and obvious conditions under consideration. These include the student’s attendance rate, how many times have they been disciplined, how many days have they been suspended, whether they have failing grades, and so on. But a data analytics program can also allow districts to evaluate student data on a deeper level.

For example:

  • What is the students GPA compared to their peers, or the other students in their school?
  • Are their grades rising or falling? By how much and over what period of time?
  • What number of their absences are considered unexcused (no communication from the parent has been received)?
  • How many times have they been consecutively absent?
  • What is their tardy rate? Studies have shown there is a clear relationship between high tardy rates and student attitude.
  • Are the student’s standardized or NCLB test scores rising or falling, and at what rate?
  • How does the student test academically?
  • How many failing grades has the student earned this year, over multiple years, and specifically, how many in 9th grade? Studies have consistently shown that the number of failing grades a student receives, particularly in the 9th grade (also associated with loss of graduation credits), are the most significant indication of a potential dropout.

In addition, the obvious should also be considered: Is the student of a demographic category that is traditionally at risk?

  • Do they have disabilities?
  • Are they a special education student?
  • Are they homeless?
  • Are they an English Language Learner?

Even the student’s gender and ethnicity should be considered. Every risk study performed shows that boys and some specific ethnicities traditionally have higher dropout rates. I would suggest that to not to consider each of these areas is to ignore reality.

There are hundreds of academic studies that outline the relationship of many of these behaviors or demographic categories to risk. Each of these studies point out the various relationships between behavior and outcome, but few address a broad number of behaviors such as I have outlined here. The approach that I suggest measures all of these areas, every day, and bases measurements on a district-managed weighting system to achieve a single measurement of risk for all students at all grade levels. At the end of this process, a district can simply refer to this one comprehensive measurement, and use it throughout all aspects of student evaluation. So, when looking at a student’s grades, attendance, test scores or discipline, a quick and encompassing measurement of how this student is performing in his or her entire school life can shine a light on how to react to that data.

I have used this approach for some time when implementing Tyler Pulse in school districts across the US. One common occurrence is this: when a principal first begins to view the students in their school who have been determined to be the most at-risk by this new and comprehensive measurement, he or she already knows the majority of those students, let’s say 17 out of the top 20. In fact, they don’t just know them; they know their phone number, their parent’s first name and their complete history, because this is a student who has been outwardly demonstrating their at-risk status by acting out or continuously failing classes. However, the principal always ends up saying, “These other three students on this list amaze me, I had no idea they were struggling.” This is the gold in this deep dive approach: you can catch those students that fly under the radar, who are asking for help so quietly that no one has been hearing them.

In a future post, I plan to discuss risk intervention programs, and how they can be targeted and managed for maximized results.

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