[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-590125-105":59,"doc-detail-590125-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","bringing-transparency-to-predictive-analytics-a-systematic-comparison-of-predictive-modeling-methods-in-higher-education","Bringing Transparency to Predictive Analytics: A Systematic Comparison of Predictive Modeling Methods in Higher Education","","Higher education increasingly uses predictive analytics to identify students at risk and to allocate additional support, yet many systems remain proprietary and provide limited visibility into the underlying models. This article addresses that transparency gap by systematically comparing modeling approaches across two dimensions: sample and variable construction and the choice of predictive modeling methods. Results evaluate how these decisions affect accuracy, stability, and the ranking of predicted completion probabilities, with attention to performance from both simpler and more complex models.",{"@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/bringing-transparency-to-predictive-analytics-a-systematic-comparison-of-predictive-modeling-methods-in-higher-education/590125/",{"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/bringing-transparency-to-predictive-analytics-a-systematic-comparison-of-predictive-modeling-methods-in-higher-education/590125.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-10-15",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},"Why does the article focus on transparency in predictive analytics for higher education?","Question",{"text":112,"@type":113},"Most predictive analytics tools used in higher education are proprietary, limiting the ability to evaluate accuracy, stability, and fairness. The article targets this lack of transparency and associated risks for institutions and students.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What two main dimensions does the article compare?",{"text":117,"@type":113},"It compares (1) approaches to sample and variable construction and their impact on model accuracy, and (2) how selecting different predictive modeling methods affects performance and stability of predicted scores.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the findings suggest about model complexity and performance?",{"text":121,"@type":113},"The article observes gains from using samples structured to represent typical enrollment patterns and using a robust set of predictors, while also finding similar performance between the simplest and most complex models.","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},590125,1792645662,{"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":56,"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,"preview_page_count":4},1374404737137,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","AERA Open  \nJanuary-December 2021, Vol. 7, No. 1, pp. 1–19 DOI: 10. 1177/23328584211037630  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2021. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero)  \nBringing Transparency to Predictive Analytics: A Systematic Comparison of Predictive Modeling Methods in Higher Education  \nKelli A. Bird  \nBenjamin L. Castleman  \nUniversity of Virginia  \nZachary Mabel  \nHarvard Graduate School of Education  \nYifeng Song  \nUniversity of Virginia  \nColleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, affects model performance and the stability of predicted scores. The relative ranking of students’predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and the most complex models.  \nKeywords: predictive analytics, college success, data science, community colleges  \nPredictive analytics have become increasingly common in the education sector. Colleges and universities use predictive analytics for various purposes, ranging from identifying students who might default on their loans to targeting alumni who are likely to give generously to the institution (Ekowo & Palmer, 2016). The most common use of predictive analytics, however, is to identify students at risk of failing courses or dropping out (Alamuddin et al., 2019; Milliron et al., 2014; Plak et al., 2019), and to direct various student success strategies (e.g., intrusive advising, additional financial aid) to these students. Numerous contextual factors have motivated institutions to turn toward predictive analytics. While enrollment rates have increased steadily over the past decade and socioeconomic inequalities in college participation have narrowed (U.S. Department of Education, 2019), completion rates remain relatively stagnant and socioeconomic disparities persist and have widened over time (Bailey & Dynarski, 2011; Chetty et al., 2020) . Students are borrowing a record  \namount of money to fund their postsecondary education—total student debt now exceeds $1 trillion—with default rates highest among students who drop out before finishing their degree (Bastrikin, 2020; Looney & Yannelis, 2015) . In light of these trends, state and federal policy makers have put increasing pressure on institutions to increase completion rates.  \nDespite this increased pressure, at broad-access institutions attended by most undergraduates, the level of resources available to invest in completion strategies has declined considerably over time as states have reduced their appropriations to public higher education (Deming & Walters, 2017; Ma et al., 2017) . The use of predictive analytics in higher education has the potential to increase efficiency in how scarce resources are allocated by targeting students who may benefit most from additional intervention. Adoption of predictive analytics strategies has been broad and rapid; a third of all institutions have invested in predictive analyticsand collectively spend hundreds of millions of dollars on  \nCreative Commons Non Commercial CC BY-NC: This","cbCairltxhDPQlUh","https://ap.wps.com/l/cbCairltxhDPQlUh","pdf",1722435,"English","# Bringing Transparency to Predictive Analytics\n## Predictive Analytics in Higher Education\n## Rationale for Transparency and Systematic Comparison\n## Dimensions Compared in Predictive Modeling","[{\"question\":\"Why does the article focus on transparency in predictive analytics for higher education?\",\"answer\":\"Most predictive analytics tools used in higher education are proprietary, limiting the ability to evaluate accuracy, stability, and fairness. The article targets this lack of transparency and associated risks for institutions and students.\"},{\"question\":\"What two main dimensions does the article compare?\",\"answer\":\"It compares (1) approaches to sample and variable construction and their impact on model accuracy, and (2) how selecting different predictive modeling methods affects performance and stability of predicted scores.\"},{\"question\":\"What do the findings suggest about model complexity and performance?\",\"answer\":\"The article observes gains from using samples structured to represent typical enrollment patterns and using a robust set of predictors, while also finding similar performance between the simplest and most complex models.\"}]","Bringing Transparency to Predictive Analytics: A Systematic Comparison of Predictive Modeling Methods in Higher Education | PDF",1792068854,48]