By Hank Bohanon (Loyola University of Chicago), Todd Winton, Sam Cusworth, and Sheryl Healy (Beaudesert State High School, Queensland, AU)
No matter what type of doctor you see, they usually collect similar information. Even before the doctor enters the room, someone will assess your blood pressure, weight, height, and temperature. These are markers related to your health. If your blood pressure or temperature is too high, or perhaps your weight has gone up or down drastically since your last visit, the doctor will know something in your treatment plan needs to be addressed. Like screening for these essential health markers, schools can also use data to see how the larger system is working. These screening tools also help teams determine if additional data are needed to solve student-specific needs. In many cases, schools may already be collecting significant amounts of information about their students they can use for decision making.
Unfortunately, having the data on your computer or in a desk drawer is not enough. Once you have identified the needed data, you must find ways to organize them. The organization of your information needs to be in a format that can help teams determine if an issue is schoolwide (tier one), involves groups of students (tier two), or is more individualized (tier three). Using graphs and tables helps your team make effective decisions around their data. Also, graphs and charts work best to monitor your overall system. For example, you may wish to see what percentage of your students are meeting target outcomes in academics and behavior. If the percentage of students is below 80%, you may need to strengthen schoolwide, tier-one strategies. Sometimes referred to as universal supports, tier-one approaches are generally applied to all students by all staff members.
Using Tables to Identify Student Needs
Tables tend to work well when identifying specific student needs. They allow you to organize data in formats that support your team’s decision-making processes. The table below provides an example from one combined middle school and high school in Vermont. The team in this example implemented schoolwide academic and behavioral interventions as part of their school improvement approach. Their schoolwide intervention team reviewed their universal data three times per year to determine if their tier-one instruction was effective, and to identify students needing additional support. The key markers they selected for their reviews included:
- Common Core Measures of Academic Progress (MAP) to inform instruction in both English language arts (ELA) and mathematics for all students.
- Student Risk Screening Scale (SRSS) to help identify students who are at risk academically, behaviorally, socially, and emotionally. The team used this tool to identify students at risk of internalizing and externalizing behavior problems.
- English language arts grades
- Math grades
- Office discipline referrals
The team wanted to ensure they identified students who needed additional support. The data were organized into a table format using a color-coding system to flag students into four categories. The colors codes were:
- Green – Continue with the universal (tier-one) instruction without additional support
- Yellow – Potential area for concern (tier two)
- Red – Significant area of concern and a support plan is needed (tier three)
- Blue – More than two grade levels above the proficient score, the student may require additional challenge and support
The team also coded the students’ names by the level of support they had already received. The colors were:
- Blue – The student had an individualized education plan (IEP)
- Maroon – the students are supported by a Section 504 plan from the Vocational Rehabilitation Act of 1973
- Orange – the student already received tier-two or tier-three level interventions.
Color coding the students’ names helped the data team know what support the students had already had in place. For example, a review of a student’s MAP test scores may have indicated she needed a support plan, as indicated by the color red in the data section of the chart. Also, her name was shaded blue, indicating that she received special education services. Based on these data, the team might review the student’s current IEP to see if adjustments needed to be made to the plan. The data included in the table aligned with the questions the team wanted to know about their students’ needs.
While the table above may reflect several important data components for your school, your team might also want to consider additional data for decision-making. For example, given the predictive value of attendance on student risk, you might add a column on the number of days the student has unexcused absences. Unexcused absences, compared to excused absences, may provide the best prediction of the future risk of failure or dropping out. There are also additional ways teams can organize their data for decision-making.
Office Discipline Referral Heat Map
Sam Cusworth and his colleagues at Beaudesert State High School in Australia developed an office discipline referral (ODR) data “heat map.” The map’s purpose is to help department chairs better understand when discipline problems were occurring in classes under their leadership. The map is also color coded on a continuum to help chairs quickly identify areas of concern (and areas for celebration). In this example, the document includes an overview of all the ‘major’ behaviors demonstrated by students at key points throughout the day.
In their approach, teachers refer students who engage in major-level behaviors (e.g., persistent defiance, fighting) to their specific curriculum area department head for support. Classroom teachers manage minor behaviors that include disrespect, defiance of authority, and not being prepared for learning. This Excel report can then be manipulated so that the curriculum area department heads can see where there are specific concerns in their subject-area classes across the week. The heat map helps department chairs, and perhaps deans, identify the best times to visit classrooms to support the implementation of tier-one practices. These practices can include increasing student engagement, providing effective instruction, or improving classroom management. A key point here is that these data are not used for punitive purposes but to support faculty and students. The department heads use these data to create more equitable and supportive environments for all students.
Other Resources
I have outlined resources in other blogs related to using data for decision-making in secondary schools (link). Also, the Institute for Education Sciences (IES) has a wonderful list of data decision-making tools that schools can apply (link). My favorite resource on their list is the infographic called Data Driven, not Data Drowning (link). This infographic is an excellent overview for schools on effectively using data for decision-making. Additionally, WestEd has a very helpful brief on using data from early warning systems to guide your intervention work (link). Early warning systems are designed to identify students at risk of dropping out and include similar data to what I mentioned in this post. As will all data systems, you should link your early warning systems to your schoolwide intervention goals.
Additionally, I think the Comprehensive, Integrated Three-Tiered Model of Prevention (Ci3T) model has one of the best overviews of using systematic screening data to identify risks related to internalized and externalized student behaviors (link). Their site includes resources for screening from elementary to high school levels. It is one of the most comprehensive and practical overviews I have seen for school teams to use for their data-based decision-making.
Just like for your doctor, using data to plan interventions is an effective way to guide your efforts. I hope this post has been helpful for your data decision-making efforts. I would love to know if you use similar systems in your schools for decision-making. Please leave a comment below!