Solving for Why: How Improved Risk Identification Reduces High School Dropout

Posted by Jeff Watson on Oct 10, 2017 8:51:58 AM

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Identifying at-risk students early on facilitates meaningful interventions and higher graduation rates.

 

Nearly six percent of 16-24 year olds are defined as dropouts. That’s over 1.8 million young adults who are not graduating from high school. And while these rates have declined over the past ten years, the implications of high dropout rates in K-12 schools are immense — for districts, teachers, society at large, and of course the students themselves.

 

Accurately identifying at-risk students is vital for classrooms, schools, and districts to meaningfully reduce the rate at which students drop out. But when it comes to pinpointing precisely which students are most likely to require intervention, there’s no cut and dry profile — a reality that makes it difficult for teachers and educators to identify risk based purely on interpersonal data. Many at-risk students show no visible signs of stress, stay off the radar, and quietly drop out two years into high school. Others will simply lapse into a pattern of failing and retaking courses, causing them to graduate late or to give up altogether.

 

But when educators take steps to identify risk early and enact meaningful interventions, they can empower and work alongside students and parents toward positive and achievable outcomes.

Why Early Identification Matters

The time from risk factor to dropout varies from student to student. For some, it may take years of perpetual negative feedback until the student is demoralized and too far behind to graduate on time. They feel buried and hopeless and ultimately, they don’t see the point in continuing. Alternatively, a dropout might be the result of a decision based on an isolated incident (e.g. a change in the family’s financial situation, a personal tragedy, etc.).

 

Because the trajectories leading to a dropout vary so widely, it’s imperative that educators be able to identify common signs extremely early on, understand the correlations between different signs and the types of intervention necessary, and address the problem before a dropout occurs.

 

Identifying risk early also enables educators to tailor intervention programs to each student’s unique cause of risk. Just as there’s no single profile for an at-risk student, there’s no single set of intervention steps that can be applied to all students. If educators can identify risk factors before they’ve matured into a dropout decision, teachers and administrators can work with the student on a personal level to help him or her succeed.

The Issues with Traditional Risk Identification Systems

Traditionally, risk identification has been siloed from year to year and teacher to teacher. While individual teachers may track a student throughout a given school year or semester, it has been consistently difficult to create an aggregated student profile available to educators across the school or district. Furthermore, even if a student is marked as needing specific attention on a school-wide level, the approach that each teacher takes will be inherently different — a fact that’s only exacerbated by the lack of a standardized model for holistic intervention.


On a macro or district-wide level, traditional risk identification systems have proven even more problematic. Because they are either largely paper-based or a combination of paper and digital records, collecting information and accurately noting trends over time is extremely difficult. This makes evaluating the efficacy of intervention efforts tedious and prone to inaccuracies, if not altogether impossible. 

How Data-Driven Platforms Can Catch Risk Factors the Minute They Appear

Data-driven platforms offer a systematic and unbiased framework for identifying risk early and crafting effective interventions. Using a proactive, data-driven system offers several much-needed improvements over the traditional system:

 

  • A holistic student profile for educators to track performance, risk factors, and interventions over time.
  • Identification of district-wide patterns of risk behavior.
  • Updated data in real-time, enabling educators to act quickly.
  • Predictive analytics that identify risk levels and causes based on past patterns.


Because data-driven platforms offer educators a closely monitored, holistic profile of each student over the long-term, they can identify risk the minute it appears with an eye toward other students’ past behaviors that have led to a dropout. Precise predictive analytics give educators the timely and cohesive information they need to develop and implement a meaningful intervention.

Use Early Warning Technology to Facilitate Meaningful Interventions

No matter a risk identification system’s level of sophistication, it can only facilitate the necessary interactions between teacher and student. Data and dashboards represent essential tools in any educator's arsenal, but they can never replace a relationship-based educational model.

 

Real, lasting change requires creating a data-informed educational environment where risk signs are identified, tracked, and acted upon. Data helps educators do what they do best for their students, schools, and communities: help more children graduate and create brighter futures.

 

We have much more coming on this topic. Make sure to get our latest straight to your inbox.

 

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Topics : Data & Analytics, Early Warning & Intervention

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