Educators across the country are seeing a greater need to collect and use data to inform decisions as they work to help students. The pandemic severely disrupted our schools, and many districts used any student data they had to identify who was struggling and how to best provide support.
As districts continue to respond to the evolving circumstances of the pandemic, making the most of all available data to improve student outcomes remains critical to understanding the factors that most contribute to students’ success.
The power of using data is immense. When used properly, it can help districts make vital decisions about setting goals and providing targeted support for students. Whether you are new to data analytics in K-12 or a seasoned veteran, here are three practical ways to apply data to help drive better student outcomes.
1. Use Data to See a Holistic Picture to Identify and Support At-Risk Students
Educators can and should use data to gain a holistic view of each student. One data point from a single observation never tells a student’s full story. Capturing a student’s academic, behavioral, attendance, and engagement data can provide a deep, informed understanding of who the student is, where they are succeeding, and where development is needed. Dashboarding data from different areas of interest can often illuminate trends and early warning signs, lending information to identify which students might need support.
A middle school in Mississippi sought to visualize data based on their homegrown at-risk model comprised of three categories: attendance, discipline, and grades. Each category had its own risk score ranging from zero to three. Combining all three categories generated a total possible risk score ranging from zero to nine. See chart Custom At-Risk Criteria below for reference. For attendance, missing five or six days of school would yield an attendance risk of two, trending toward high risk for absences. Assuming that same student missed no additional days of school, had no disciplinary events, and all of his grades were higher than 70, their total at-risk score would remain two.
Specifying a unique and multi-tiered rubric for each risk category provided a rich amount of information and a natural way to parse and analyze data. In this instance, school administration discovered that chronic absenteeism accounted for the most risk among their student population, with 97% of students having at least one risk point attributable to absences. Disciplinary events were overall negligible, with few overall risk points coming from this category. Risk based on low performance in the classroom revealed an interesting but troubling pattern. Though few students were at risk due to having low classroom grades, most students within this group had an overall high-risk score (an average of six). Moreover, this data revealed that students who were failing one classroom subject were usually failing at least one other subject as well.
|# Absences||# Infractions||# Grades Below 70||Score|
|0 – 1||0||0||0|
|2 – 4||1 – 2||1||1|
|5 – 6||3||2||2|
|7 or more||4 or more||3 or more||3|
|0 – 3||0 – 3||0 – 3||0 – 9|
Filtering and comparing results by grade level and other demographic factors allowed educators to see if differences emerged based on students’ current circumstances (e.g., experiencing homelessness or being in an after-school program). In other words, this data informed whether some students, more than others, were more or less frequently observed as overall high risk or high risk by particular categories.
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