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.
2. Use Data to Set Goals and Target Interventions
One thing most educators can agree on is that all students learn differently. Thus, student goal setting and interventions are often tailor-made to the unique needs of each student. Seeing where a particular student falls on the at-risk model enables educators to set specific, individualized goals for students and target interventions where most needed. While each school will face unique challenges, data can help all schools spot trends and determine areas of priority.
Continuing with the prior example, having set and applied clear criteria to identify at-risk students, the district could then turn its attention to implementing an intervention. An example could be to reduce the number of students acquiring the highest risk score the following year, and set a goal of meeting a specific (but reasonable and attainable) threshold of success for the next three years (e.g., reduce by 10 percent the first year). One such approach includes selecting one or two areas of focus at one or two campuses initially. Further breaking down results of the at-risk model by individual categories would inform which areas of risk are the highest priority, and would offer the most return on investment if successfully targeted for intervention. In the current example, students with failing grades tend to have the overall highest score, suggesting a potential area to target, or seek further information.
3. Use Data to Support Meaningful Parent-Teacher Communication
Sharing the students’ data with their families can help to drive ongoing meaningful communication between school and home. Using data, educators can inform parents about their students through data-driven conversations.
Offering specific information about a child’s attendance or behavior can help with creating more meaningful relationships with home. Having data enables teachers to talk specifically about the number of attendance or behavior incidents for a student in a month and have a two-way conversation with a parent about supporting the child’s improvement. For instance, being able to see that a student has been chronically absent this year, but has never had an attendance problem in past years suggests the family might be experiencing a unique and stressful situation. Only with data at hand would this insight be possible, allowing the opportunity for the educator to ask if everything is okay at home. On a lighter note, having student-level data that is longitudinal and includes both quantitative and qualitative data offers a wonderful opportunity to see how a student has progressed over time, which is always a fun conversation to have with caregivers.
There is great potential for data to serve as a tool for educators and administrators who want to improve schools and help all students reach their potential. Collecting and analyzing student-data can help create a holistic view of a student, set appropriate, individualized goals and targeted interventions, and support meaningful parent-teacher communication. This can all help students along the way and ensure greater outcomes.