When it comes to early warning and intervention, new technological approaches are offering educators more options and even more insights.
Staging effective interventions for at-risk students poses challenges for school districts of every size. Administrators, teachers, and parents face the task of identifying which students need attention, what sort of attention they need, and whether the attention they received was successful in getting them back on track.
This may seem more than feasible in the abstract. But in reality, educators must contend with federal mandates, funding shortfalls, and outdated progress-tracking mechanisms when working to design the right interventions for at-risk students. Traditional responses to this dilemma may have offered stopgap solutions, but new classroom pressures — including the need to ensure every student is college and career ready — only exacerbate the challenge of quickly staging effective interventions.
However, new technological approaches to intervention represent a way forward. Improved analytics platforms can help teachers and intervention specialists more quickly identify students with skill gaps, understand the effectiveness of actions taken in the past, target the actual needs of each student, and easily share the results in order to ensure consistent growth. For administrators and educators — and the communities they serve — this technology is essential.
Three of the most common forms of traditional intervention are teacher in the classroom-based, pull out for intervention, and behavioral. In the classroom, teachers can be tasked with reteaching skills, in the moment or in small group, and correcting behavioral issues with consequence. In pull out or in small groups, intervention specialists can work with a limited number of students with different skill gaps in order to give them the extra attention they need. And, finally, schools can utilize their own disciplinary procedures for students whose issues are mainly behavioral.
These approaches offer districts little precision as they seek to determine when and how they should stage, monitor, and resolve interventions — and can function more as guesswork than science.
In the classroom approach, for example, teachers have to scramble between moving their lesson plan forward for other pupils while reteaching skills or correcting behavioral issues for at-risk students. Without institutional insight into what interventions have worked best in the past (and for what types of students each type of intervention has worked), they’re often left scouring online for resources in an effort to find the best options for their kids.
Using the small group model, intervention specialists can provide at-risk students with the attention they need, but if each student has unrelated skill gaps or behavioral problems, this blanket approach to intervention may not be effective. And, if the intervention doesn’t work for some or all of the students, it’s challenging to determine whether the invention itself was insufficient, or whether it was simply the wrong choice for the students in question.
Using the disciplinary approach, administrators run the risk of addressing surface-level behavior without treating the root cause of an issue. Rather than treating behavioral incidents as isolated outbursts, continued monitoring of an individual’s behavior can lead to valuable insights as to what’s going on under the surface.
Each of these models is reflective of two overarching flaws with traditional methods of intervention: 1) The absence of timely, relevant data with the potential to reveal precisely what may be driving student performance or behavior, and 2) The inability to track progress and determine whether an intervention has been successful. As a result, students are often placed into an intervention for a designated period of time, regardless of severity of need or efficacy of the intervention.
The way most districts currently gauge the effectiveness of an intervention is through an assessment administered at the end of the grading period, quarter, semester, or year. The problem with this assessment-driven measurement is that it offers no sense of what actually helped that student close their skill gap and achieve mastery. It’s challenging to trace a clear path to their success, which means educators can’t apply learnings from this intervention in the future.
Technology enables educators to continuously monitor progress and tie incremental improvements to specific interventions, helping them understand what works and what doesn’t for each student. These records can then be used as a blueprint for future interventions, and guide districts in designating specific benchmarks that educators can use to evaluate student progress and determine whether an intervention should be concluded.
Equipped with a better understanding of which interventions are working and why they’re working, administrators and intervention specialists can more accurately assess the efficacy of their efforts — and perhaps most importantly, get kids back on track more quickly.
Learn more by downloading our latest white paper, Facilitating Effective Interventions: Using Data-Driven Insights to Transform Your Processes.