Across the United States, chronic absenteeism (defined as missing ten or more days per school year) among K-12 students is at an all-time high. Studies show that students’ reasons for chronic absenteeism tend to fit into four categories:
- External factors like having to work a job, having to provide translation for a parent or guardian, or lacking transportation to school.
- Avoidance of school or the route to school or school itself, often due to safety concerns.
- Disengagement from school or the people who work at school.
- Misconceptions, usually from parents, as to when students are supposed to be kept at home and when it is safe to send them to school.
While the reasons for chronic absenteeism can be classified into these four relatively tidy categories, individual cases of absenteeism aren’t always so clean cut. Similarly, strategies for addressing each of these categories vary in their effectiveness depending on each student’s unique needs.
For this reason, the first step in creating a plan to address chronic absenteeism is high quality data collection. Not only does data collection assist districts and states with developing approaches for meeting their population’s specific needs, it also is invaluable for schools working on a student-by-student basis.
When designing a data collection strategy, make sure to include the following in any sort of investigation:
- Collect and disseminate data frequently. Currently, schools, states, and districts tend to share absentee data once a year. Data should be collected daily and shared on a monthly or (ideally) a weekly basis. Frequent data collection and dissemination not only allows districts and states to recognize trends early, it also allows schools to engage in early interventions with students before they end up with ten absences – a practice that is especially key since studies show that chronically absent students can, inadvertently, begin a trend of chronic absences among their peers.
- Disaggregate data as much as possible. Data collection must go beyond simply reporting the number of students absent. Instead, data should be disaggregated by grade level and other demographic categories like race, gender, disability status, and class, among others. When analyzing data, also consider comparing it to absence rates for individuals and classes during previous school years, students’ attendance during the first month of school, and, if possible, the number of positive relationships they have with adults in the building. Again, disaggregating data makes it easier to notice patterns before they become too unwieldy to address.
- Collaborate on data analysis and response. Districts, states, and schools should work together to identify and respond to statistical trends. Additionally, these trends should be shared with community partners who can provide expertise in addressing underlying issues causing absences. For example, if a large number of absences are due to illnesses, a school or district may want to partner with local clinics to improve healthcare. If it seems that students who are unhoused are missing school more than housed students, it makes sense to partner with housing services. Experts in these fields might have innovative ideas about providing students with the services they need to stay in school.
- Develop new measures to evaluate programmatic success. In addition to collecting data about student absences, schools, states, and districts should collect and analyze data related to programmatic effectiveness. This real time testing gives schools immediate feedback about which interventions are the most effective at curbing absences, and which might not be worth pursuing. This is also useful at the school level, where understanding students’ and families’ unmet needs are key to helping them keep their children in school.
