[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-203463-105":59,"doc-detail-203463-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","using-predicted-academic-performance-to-identify-at-risk-students-in-public-schools-april-2023","Using Predicted Academic Performance to Identify At-Risk Students in Public Schools - April 2023","","Measures of student disadvantage are central to equity-focused education policies, yet common risk indicators used today have major limitations and have seen little innovation despite advances in data infrastructure and analytics. The paper develops Predicted Academic Performance (PAP), a flexible, data-rich risk measure that combines early warning concepts with incentive design principles. Proof-of-concept simulations using Missouri data show PAP more effectively identifies students at risk and supports more efficient targeting of resources across multiple associated risk categories.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-predicted-academic-performance-to-identify-at-risk-students-in-public-schools-april-2023/203463/",{"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/using-predicted-academic-performance-to-identify-at-risk-students-in-public-schools-april-2023/203463.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address about existing education risk measures?","Question",{"text":112,"@type":113},"Current policies rely on blunt categorical indicators of disadvantage, which do not precisely define “at risk” and do not fully use rich student data to improve risk measurement.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is Predicted Academic Performance (PAP) designed to measure student risk?",{"text":117,"@type":113},"PAP is a flexible, data-rich indicator that defines risk as poor academic performance and blends early warning system concepts with incentive design to balance accuracy and policy usability.",{"name":119,"@type":110,"acceptedAnswer":120},"What evidence is provided to evaluate PAP?",{"text":121,"@type":113},"The authors run proof-of-concept policy simulations using Missouri data, showing PAP is more effective than common alternatives at identifying students at risk and can target resources more efficiently.","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},203463,1788560950,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Using Predicted Academic Performance to Identify  \nAt-Risk Students in Public Schools  \nIshtiaque Fazlul  \nCory Koedel  \nEric Parsons  \nApril 2023  \nMeasures of student disadvantage—or risk—are critical components of equityfocused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new measure of student risk for use in education policies, which we call Predicted Academic Performance (PAP) . PAP is a flexible, data-rich indicator that identifies students at risk of poor academic outcomes. It blends concepts from emerging early warning systems with principles of incentive design to balance the competing priorities of accurate risk measurement and suitability for policy use. In proof-of-concept policy simulations using data from Missouri, we show PAP is more effective than common alternativesat identifying students who are at risk of poor academic outcomes and can be used to target resources toward these students—and students who belong to several other associated risk categories—more efficiently.  \nAffiliations and Acknowledgement  \nFazlul is in the Department of Economics, Finance, and Quantitative Analysis at Kennesaw State University, Koedel is in the department of economics and Truman School of Government and Public Affairs at the University of Missouri, and Parsons is in the Department of Economics atthe University of Missouri. We thank the Missouri Department of Elementary and Secondary Education for access to data, Rachel Anderson and Alexandra Ball at Data Quality Campaign for useful comments, and Andrew Estep and Cheng Qian for research support. We gratefully acknowledge financial support from the Walton Family Foundation and CALDER, which is funded by a consortium of foundations (for more information about CALDER funders, [see](see www.caldercenter.org/about-calder)[ ](see www.caldercenter.org/about-calder)[www.caldercenter.org/about-calder](see www.caldercenter.org/about-calder)). All opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the funders, data providers, or institutions to which the author(s) are affiliated. All errors are our own.  \n1. Introduction  \nThere have been substantial advances in education data infrastructure since the turn of the 21st century, and as of our writing this article, virtually every state in the U. S. has a state longitudinal data system (SLDS) supported by large investments from the federal government.1 These data systems allow states to track students as they move through K-12 schools, monitoring their academic progress and providing rich information about their circumstances. Computing power has also increased rapidly during this same period of data infrastructure investment, so not only are rich data on K-12 students increasingly available, they are also increasingly usable.  \nHowever, these gains in data availability and useability have not translated into meaningful improvements in how states identify students in need of additional resources and supports, who are commonly described as being “disadvantaged” or “at risk”(we use these terms interchangeably throughout this article) . Today, as has been the case for decades, states ubiquitously rely on blunt categorical indicators associated with disadvantage to identify these students. Examples of common indicators include free and reduced-price meal (FRM) enrollment, direct certification (DC), English language learner (ELL) status, individualized education program (IEP) status, and underrepresented minority (URM) status, among others.  \nThe categorical approach to identifying at-risk students is limited in a number of ways. To illustrate with an example, consider California’s Local Control Funding Formula (LCFF), which identifies at-risk students categorically based on whether","cbCaiiTyfViAk6Zr","https://ap.wps.com/l/cbCaiiTyfViAk6Zr","pdf",738008,61,"English","# Introduction\n## Limitations of Categorical Risk Measures\n## Development of Predicted Academic Performance (PAP)\n## Proof-of-Concept Using Missouri Data","[{\"question\":\"What problem does the paper address about existing education risk measures?\",\"answer\":\"Current policies rely on blunt categorical indicators of disadvantage, which do not precisely define “at risk” and do not fully use rich student data to improve risk measurement.\"},{\"question\":\"How is Predicted Academic Performance (PAP) designed to measure student risk?\",\"answer\":\"PAP is a flexible, data-rich indicator that defines risk as poor academic performance and blends early warning system concepts with incentive design to balance accuracy and policy usability.\"},{\"question\":\"What evidence is provided to evaluate PAP?\",\"answer\":\"The authors run proof-of-concept policy simulations using Missouri data, showing PAP is more effective than common alternatives at identifying students at risk and can target resources more efficiently.\"}]","Using Predicted Academic Performance to Identify At-Risk Students in Public Schools - April 2023 | PDF",154]