A crucial trend in today's educational climate is rapidly and routinely identifying students at risk of dropping out of school. Often, the data you need to identify these students is spread across multiple locations and formats, where it is difficult to see at a glance.
Need to identify at-risk students faster?
Based on research studies into at-risk students, Scantron combines ten indicators in our Early Warning System. Attendance rates, behavior incidents, and course performance (commonly referred to as the ABCs of early warning) are all included as indicators. All of this data is tracked by marking period so you can identify and respond to issues as they occur.
Scantron Analytics Early Warning System presents these indicators as part of an interactive, highly visual dashboard, so you can drill down to individual students and determine suitable interventions to prevent droupouts based on real data—fast.
Require customized at-risk indicators?
While a number of at-risk indicators are common across schools, studies also recognize some variation in indicators by school and district. As a result, the Scantron Analytics Early Warning System is configurable, so you can add the indicators and threshold values you have found to be most reliable.
You can also configure the definitions of “At-Risk” and “On Track” as you may prefer to set these higher or lower, depending on your needs.
Want to spot changing student dropout risk profiles?
It can be critically important to spot students whose dropout risk profile has increased between marking periods. For example, a student who was on track in Marking Period 1 may have fallen behind by Marking Period 3. The Early Warning System makes it easy to identify such students—as shown in the chart below.
Large red, yellow, and green bubbles illustrate students whose risk status has not changed. Smaller bubbles indicate students whose risk has changed. With a single click, you can drill into the details of these students, seeing exactly what indicators are causing their risk status to change.
Need to view at-risk data for differing student populations?
At a district level, it’s often important to be able to disaggregate your at-risk student population, understanding how it varies by ethnicity, gender, and meal status, as well as understanding differences across high schools and grades.
The chart below shows that such disaggregation is at your fingertips with Scantron Analytics Early Warning System.
You can generate different views of disaggregated data with a single click, changing between a view by ethnicity (shown) to similar views by gender, meal status, and more.