Early warning systems, responses to intervention, and multi-tiered systems of support are all critical to providing each and every student with a well-tailored education.
Dropout rates among 16- to 24-year-old Americans have steadily declined over the last 20 years. For example, the National Center for Education Statistics recently reported a drop in the status drop out rate from 10.9 percent in 2000 to 5.9 percent in 2015. Much of this progress is at least partially attributable to the proliferation of increasingly sophisticated early warning systems — and the variety of interventions that have accompanied them.
But while we’re certainly headed in the right direction, more than half a million students still drop out of high school every year. As a nation, we must continue to become more adept at both spotting and assisting struggling students across a variety of different circumstances. Doing so will require teachers and administrators to take a more holistic view of every student and continue to develop early warning systems to measure and respond to individual students’ needs and risks.
The Department of Education (DoE) defines an early warning system as “a system based on student data [used] to identify students who exhibit behavior or academic performance that puts them at risk of dropping out of school.” According to DoE data, 52 percent of public high schools in the country had some sort of early warning system in place during the 2014-2015 school year.
While there is a great deal of variation among the early warning systems that districts employ, most systems focus on the so-called “ABCs” of student risk: attendance, behavior, and course performance. In fact, 92 percent of public school early warning systems track student attendance, 91 percent track course grades, and 79 percent track major disciplinary incidents.
Different districts craft their warning systems around different metrics, but at the end of the day, their common goal is to use diverse, often seemingly unrelated datasets as a means of pinpointing at-risk — or already struggling — students as early as possible. The sooner teachers and administrators recognize that something is wrong — and the more they understand the nature of the problem (academic, behavioral, etc.) — the more effective their responses tend to be.
EW systems should strive to measure variables of multiple time frames (eg., long-term attendance versus short-term attendance) and with a little latency as possible. The main justification for this approach is that students follow different trajectories to dropping out of school. Some students will display warning signs very early and for a very long period of time while others will drop out over a very short period of time (e.g., decide to work full-time).
RTI is a three-tiered action taken in response to an early warning system alert that becomes progressively intensive and personalized as it unfolds. Tier 1 RTI takes place in a normal classroom setting and involves a teacher screening each of their students for underdeveloped skillsets. Generally speaking, as many as 75 to 80 percent of students need some sort of additional instruction to meet grade-level expectations across all subjects, so it’s usually feasible for a teacher to provide Tier 1 RTI without significantly deviating from their original lesson plans.
Typically Tier 2 will target about 10-15% of students with supplemental support. For example, a group of students may receive an intervention while the rest of the class goes to “specials” like physical education or art class. In this smaller, more intimate setting, a teacher will experiment with alternative instructional methods, operating under the assumption that the methods used during general instruction were ill-suited to these particular students’ needs.
For a small fraction of students — usually, 5 to 7 percent — Tier 2 RTI will also fail to get them back on track. In Tier 3, struggling students receive highly-tailored, often one-on-one instruction. Students receiving Tier 3 RTI will usually continue to participate in the bulk of general classroom instruction, but they will also be pulled out of class as needed.
While they are frequently conflated, RTI and MTSS are not the same thing. Granted, in almost every instance, RTI is a part of MTSS, but MTSS covers a much broader range of actions than academic interventions. Borrowing heavily from RTI’s three-tiered structure, MTSS provides students with tiered social and emotional support, including positive behavioral intervention and support (PBIS) measures like behavior intervention plans.
In short, MTSS is an overarching framework designed to provide the support and remediation services necessary for schools to educate the whole child. Reducing dropout rates even further will require attending to students’ needs from every angle, and a good MTSS provides educators with the structure they need to do so.
Early warning systems lay the groundwork for RTI and MTSS by alerting educators to which students require additional attention and why. Early Warning systems can play the role of a screener or diagnostic by informing the educator of the severity of a student’s risk as well as why that student is at risk. RTI and MTSS then provide a response framework that can be tailored to the needs of each student. By leveraging a data-driven early warning system to identify at-risk students as early as possible, educators can craft individualized interventions that help students get back on track in plenty of time.
Equipped with the who and the why that early warning systems provide, districts will be better able to transform student outcomes and, ultimately, improve graduation rates. What’s more, reviewing EWS data over time makes it possible for administrators to determine the overall effectiveness of intervention programming across their district, schools, cohorts, and individual students.
Ultimately, leveraging early warning systems, RTI, and MTSS effectively comes down to efficient, reliable data management. In addition to simplifying data collection and organization, Hoonuit’s intuitive early warning solution gives educators access to a powerful predictive analytics tool capable of uncovering at-risk students who might go unnoticed by more traditional performance monitoring mechanisms.
With Hoonuit, teachers can easily match a student with the ideal intervention — and track the student’s progress once the intervention goes into effect — guaranteeing that each and every student receives the well-tailored, just-in-time education they deserve.