A new predictive tool created by researchers at the American Enterprise Institute allows school districts to identify students at risk of chronic absenteeism before the school year begins. The Absence Forecast uses past attendance data that districts already maintain, along with information such as current-year absences, grade level, and eligibility for free and reduced-price lunches, to generate a likelihood score between 0-100% that a student will become chronically absent.
Nat Malkus, deputy director of education policy studies at AEI and one of the tool’s developers, emphasized that the system is designed for accessibility to district leaders rather than requiring research expertise. He noted that many schools lack awareness of their ability to act on attendance data early in the school year.
Chronic absenteeism, defined as missing at least 10% of school days, has been a significant challenge for districts in recent years. National data shows that chronic absenteeism peaked at approximately 28% during the 2021-22 school year, though recent reports indicate improving trends with 34 of 41 states showing declining rates in 2024-25, though none have yet returned to pre-pandemic levels.
Testing of the tool in Indiana and Rhode Island demonstrated accuracy rates of 88-92% in predicting chronic absenteeism. The models proved effective when applied to different states regardless of variations in student populations and geography, and maintained accuracy both before and after the pandemic despite significant shifts in attendance patterns during that period.
The tool prioritizes data security by processing information locally on users’ computers rather than uploading to external servers, with options for either browser-based operation or full offline functionality. Districts can use the predictions to provide family resources, establish early communication about attendance expectations, and monitor intervention effectiveness throughout the year, adjusting strategies based on changes in student risk scores.
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