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By simulating how otherwise promising initiatives might scale into educational policy, researchers and policymakers can investigate and proactively preempt harmful side effects. The study generates hypothetical statewide scenarios for an algorithmic teacher education initiative, compares them with historical records of clinical placements, identifies likely inequities at scale, and applies algorithmic adjustments before real-world harm occurs.",{"@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/simulating-the-statewide-scaleup-of-a-promising-teacher-education-initiative-to-preempt-its-unintended-consequences-for-racial-inequity-602338/602338/",{"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/simulating-the-statewide-scaleup-of-a-promising-teacher-education-initiative-to-preempt-its-unintended-consequences-for-racial-inequity-602338/602338.png","ImageObject",300,407,{"name":92,"@type":93},"dawugda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why does the study argue that current evaluation research is insufficient?","Question",{"text":112,"@type":113},"It states that most evaluation research does not adequately anticipate unintended consequences, which can allow negative effects outside the policy’s observed scope.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study prevent unintended consequences?",{"text":117,"@type":113},"It simulates how a promising initiative would scale statewide and compares plausible scenarios with historical clinical placement records to proactively adjust the algorithm.",{"name":119,"@type":110,"acceptedAnswer":120},"What kind of initiative is simulated, and what outcomes are examined?",{"text":121,"@type":113},"The study simulates an algorithmic teacher education initiative and focuses on its potential effects on the quality of preservice teachers’ clinical placements and the racial equity implications at scale.","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},602338,1792441503,{"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":19,"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":144,"read_time":145},3985747870947,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","| education policy analysis archives\u003Cbr>A peer-reviewed, independent, open access, multilingual journal | |\n| --- | --- |\n| Arizona State University |  |\n| Volume 33 Number 85 December 2 , 2025 ISSN 1068-2341 |  |\n\nSimulating the Statewide Scaleup of a Promising Teacher Education Initiative to Preempt its Unintended Consequences for Racial Inequity  \nMatthew Truwit  \nUniversity of Louisville  \nEmanuele Bardelli  \nSanta Rosa City Schools  \n&  \nMatthew Ronfeldt  \nUniversity of Michigan  \nUnited States  \nCitation: Truwit, M., Bardelli, E., & Ronfeldt, M. (2025) . Simulating the statewide scaleup of a promising teacher education initiative to preempt its unintended consequences for racial inequity. Education Policy Analysis Archives, 33(85). [https://doi.org/10.14507/epaa.33.8972](https://doi.org/10.14507/epaa.33.8972)  \n[Abstract:](Abstract: Most evaluation research fails to adequately anticipate unintended consequences)[ Most evaluation research fails to adequately anticipate unintended consequences](Abstract: Most evaluation research fails to adequately anticipate unintended consequences), thereby implicitly permitting the possibility of negative repercussions so long as they fall out of the purview of the policy under study. Simulating how otherwise promising initiatives might scale into educational policy, however, offers both researchers and policymakers a way to not only investigate but also preemptively address any such possible, pernicious side effects. We provide an illustrative example of this kind of forward-thinking evaluation research by generating hypothetical scenarios of the statewide implementation of an algorithmic teacher education initiative shown in prior research to have positive intended effects on improving the  \nquality of preservice teachers’ clinical placements. By comparing these plausible implementation scenarios against the historical record of clinical placements that programs actually made, we are able to not only uncover the unintended but anticipated inequities that this initiative would likely introduce if adopted at scale but also proactively make algorithmic adjustments that prevent their occurrence without diminishing any intended positive impacts, all before causing realworld harm.  \nKeywords: unintended consequences; cooperating teachers; simulation; racial equity  \nSimulando la ampliación a nivel estatal de una prometedora iniciativa de formacióndocente para prevenir sus consecuencias no deseadas en la inequidad racial  \nResumen: La mayoría de las investigaciones de evaluación no logran anticipar adecuadamente las consecuencias no deseadas, permitiendo implícitamente la posibilidad de repercusiones negativas siempre que queden fuera del alcance de la política analizada. Sin embargo , simularcómo iniciativas prometedoras podrían ampliarse a nivel de política educativa ofrece tanto a los investigadores como a los responsables de políticas una vía para no solo investigar, sino tambiénabordar de manera preventiva dichos efectos secundarios perniciosos. En este estudio presentamos un ejemplo ilustrativo de este tipo de investigación evaluativa prospectiva, generando escenarios hipotéticos de la implementación estatal de una iniciativa algorítmica deformación docente que, según investigaciones previas, ha mostrado efectos positivos en lamejora de la calidad de las prácticas profesionales de los futuros docentes. Al comparar estosescenarios de implementación plausibles con los registros históricos de prácticas profesionalesque los programas realmente realizaron, podemos no solo identificar las inequidades nodeseadas pero previsibles que esta iniciativa introduciría si se adoptara a gran escala, sino también realizar ajustes algorítmicos proactivos que eviten su aparición sin disminuir los impactos positivos previstos, todo ello antes de causar daño en el mundo real.  \nPalabras-clave: consecuencias no deseadas; docentes tutores; simulación; equidad racial  \nSimulando a ampliação em escala estadual","cbCaioSlY40UlD2S","https://ap.wps.com/l/cbCaioSlY40UlD2S","pdf",457054,29,"English","# Abstract\n## Forward-looking evaluation via simulation\n## Algorithmic scenario design and comparison\n## Identifying inequities and preventive adjustments","[{\"question\":\"Why does the study argue that current evaluation research is insufficient?\",\"answer\":\"It states that most evaluation research does not adequately anticipate unintended consequences, which can allow negative effects outside the policy’s observed scope.\"},{\"question\":\"How does the study prevent unintended consequences?\",\"answer\":\"It simulates how a promising initiative would scale statewide and compares plausible scenarios with historical clinical placement records to proactively adjust the algorithm.\"},{\"question\":\"What kind of initiative is simulated, and what outcomes are examined?\",\"answer\":\"The study simulates an algorithmic teacher education initiative and focuses on its potential effects on the quality of preservice teachers’ clinical placements and the racial equity implications at scale.\"}]","Simulating the Statewide Scaleup of a Promising Teacher Education Initiative to Preempt its Unintended Consequences for Racial Inequity | PDF",1792094500,73]