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The report describes building models using common historical district data, achieving high early-year prediction accuracy, and demonstrating robust transfer across states, geographies, student populations, and multiple years including periods before and after COVID-era changes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/catching-absenteeism-early-and-often-introducing-the-aei-absence-forecast/581951/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/catching-absenteeism-early-and-often-introducing-the-aei-absence-forecast/581951.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the AEI Absence Forecast address?","Question",{"text":112,"@type":113},"It targets chronic absenteeism by identifying students likely to become chronically absent early in the school year so schools and districts can intervene sooner.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are predictions generated by the AEI Absence Forecast?",{"text":117,"@type":113},"The tool packages trained machine-learning models and uses historical district data to estimate the probability that each student will become chronically absent.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the tool handle student data privacy?",{"text":121,"@type":113},"Students’ data are not uploaded to the provider’s servers during web use; the website delivers models to the user’s device, and the offline desktop application operates entirely on the user’s computer.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},581951,1792113651,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Catching Absenteeism Early and Often: Introducing the AEI Absence Forecast  \nNat Malkusand Sam Hollon June 2026  \nKey Points  \n• Prior research shows that models can predict students’ likelihood of becoming chronically absent early in the school year using the data that school districts typically have.  \n• The AEI Absence Forecast operationalizes these models in a tool that school and district officials can use to identify the students most likely to benefit from interventions to improve attendance.  \n• The AEI Absence Forecast is designed to be private, transparent, simple, and free, and it uses data that districts already routinely collect so that school officials can intervene early and often.  \nEven though the pandemic ended a few years ago, chronic absenteeism rates remain elevated in all states and for every student demographic. In fact, the problem is so widespread that it is hard for schools and districts to know where to target interventions to improve attendance: Whom do you target when every studentis a candidate for help?  \nTo get closer to an answer schools and districts can use, last year the authors of this report partnered with the state education departments in Indiana and Rhode Island to see if we could predict which students would be chronically absent early in the school year. We built machine learning models, training them on the kinds of simple historical data that most districts already have on hand.  \nOur results showed that well-designed models could make accurate predictions—even early in a school year. Indeed, depending on when the predictions were made, the models correctly predicted whether students would  \nbecome chronically absent between 85 and 93 percent of the time. (See Figure 1.)  \nImportantly, our models also worked well outside the state each model was trained on. Indiana’s and Rhode Island’s geographies , schools, and student populations contrast sharply, but models trained on Indiana data nonetheless worked well for predicting which Rhode Island students would be chronically absent, and models trained on Rhode Island data similarly worked well for Indiana students.  \nNot only did the models work across states; they worked across years. Our models were similarly accurate in predicting chronic absenteeism that happened before and after the COVID-19 pandemic, despite the major changes to attendance that took place inbetween. Collectively, these findings gave us confidence that the models’predictions were robust enough across both geography and time to create actionable data for schools and districts. But having good models doesn’t by itself  \nAMERICAN ENTERPRISE INSTITUTE 1  \nTraining Data  Rhode Island  Indiana  \nPredictions for Rhode Island Predictions for Indiana  \nAccuracy  \n92%  \n90%  \n88%  \n86%  \nDay 0 Oct. 1 Nov. 1 Dec. 1 Jan. 1 Day 0 Oct. 1 Nov. 1 Dec. 1 Jan. 1  \nSource: Nat Malkus and Sam Hollon, Need Not Be a Surprise: Early-Warning Systems for Chronic Absenteeism, American Enterprise Institute, October 14, 2025, 13, fig. 6, https://www.aei.org/research-products/report/need-not-be-a-surprise-early-warning-systems-for-chronicabsenteeism/ .  \nget actionable data into the hands of school and district staff. So we built a website and, for those who prefer it, a downloadable application to make our best models publicly available in a tool schools and districts can use to target attendance interventions early and often.1  \nAEI’s Absence Forecast tool is not the first of its kind; others have built early-warning systems for attendance and other student outcomes, but typically, these systems have practical limitations.2 Either those building the systems use proprietary models too complicated or expensive for schools and districts, orthe models are academic, making good predictions but being even more complicated in practice. Sometimes those academic models require data that many schools and districts either don’t have or can’t easily put together.  \nWe assumed a district leader’s perspect","cbCaike8v2gkbhoP","https://ap.wps.com/l/cbCaike8v2gkbhoP","pdf",164926,"English","# Key Points\n# Predicting Chronic Absenteeism Early\n## Cross-state and Cross-year Results\n# Building a Public Tool for Districts\n## Guiding Principles\n### Keep It Private\n### Keep It Transparent","[{\"question\":\"What problem does the AEI Absence Forecast address?\",\"answer\":\"It targets chronic absenteeism by identifying students likely to become chronically absent early in the school year so schools and districts can intervene sooner.\"},{\"question\":\"How are predictions generated by the AEI Absence Forecast?\",\"answer\":\"The tool packages trained machine-learning models and uses historical district data to estimate the probability that each student will become chronically absent.\"},{\"question\":\"How does the tool handle student data privacy?\",\"answer\":\"Students’ data are not uploaded to the provider’s servers during web use; the website delivers models to the user’s device, and the offline desktop application operates entirely on the user’s computer.\"}]","Catching Absenteeism Early and Often - Introducing the AEI Absence Forecast | PDF",1792042331,15]