[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127083-en":3,"doc-seo-127083-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127083,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Collaborative Development of Machine Learning Algorithms for Student Success at John Jay College - Article","Improving graduation rates is a central objective in higher education, yet identifying students at risk of leaving requires substantial resources and can limit timely, transparent interventions. This work describes a collaboration between DataKind and John Jay College of Criminal Justice to co-create a data-assisted student success tool that uses machine learning to flag dropout risk while keeping advisors central to student support. Findings indicate that, after implementation, John Jay College experienced higher senior graduation rates, suggesting value in collaborative, human-centered data science approaches for student success.","Journal of Access, Retention, and Inclusion in Higher Education  \n\n| Volume 7  Issue 1 | Article 2 |\n| --- | --- |\n| 2024\u003Cbr>Collaborative Development of Machine Learning Algorithms for Student Success at John Jay College\u003Cbr>John Harnisher\u003Cbr>DataKind, Brooklyn, NY\u003Cbr>Sumaya Villanueva\u003Cbr>John Jay College of Criminal Justice, New York City, NY\u003Cbr>Dana Prieto\u003Cbr>John Jay College of Criminal Justice, New York City, NY Ricardo Anzaldua\u003Cbr>Katie Beem\u003Cbr>Colorado State University, Fort Collins, CO\u003Cbr>Follow this and additional works at: [https://digitalcommons.wcupa.edu/jarihe](https://digitalcommons.wcupa.edu/jarihe)\u003Cbr> Part of the Higher Education Commons |  |\n\nRecommended Citation  \nHarnisher, J., Villanueva, S., Prieto, D., Anzaldua, R., & Beem, K. (2024) . Collaborative Development of Machine Learning Algorithms for Student Success at John Jay College. Journal of Access, Retention, and Inclusion in Higher Education, 7(1) . Retrieved from [https://digitalcommons.wcupa.edu/jarihe/vol7/iss1/2](https://digitalcommons.wcupa.edu/jarihe/vol7/iss1/2)  \n[This Article is brought to you for free and open access by Digital Commons @ West Chester University. It has been](This Article is brought to you for free and open access by Digital Commons @ West Chester University. It has been)[ ](This Article is brought to you for free and open access by Digital Commons @ West Chester University. It has been)accepted for inclusion in Journal of Access, Retention, and Inclusion in Higher Education by an authorized editor of Digital Commons @ West Chester University. For more information, please [contact](contact wcressler@wcupa.edu)[ wcressler@wcupa.edu](contact wcressler@wcupa.edu).  \nCOLLABORATIVE ALGORITHMS FOR STUDENT SUCCESS 1  \nCollaborative Development of Machine Learning Algorithms for Student Success at John  \nJay College  \nJohn Harnisher 1, Sumaya Villanueva2, Dana Prieto2, Ricardo Anzaldua2, Katie Beem 1  \n1 DataKind, New York City, NY  \n2 John Jay College of Criminal Justice, New York City, NY  \nAuthor Note  \nJohn Harnisher [https://orcid.org/0009-0003-8723-1986](https://orcid.org/0009-0003-8723-1986)  \nSumaya Villanueva [https://orcid.org/0009-0003-0051-4642](https://orcid.org/0009-0003-0051-4642)  \nDana Prieto[https://orcid.org/0009-0008-7991-330X](https://orcid.org/0009-0008-7991-330X)  \nKatie Beem  [https://orcid.org/0009-0004-3790-9944](https://orcid.org/0009-0004-3790-9944)  \nWork on this submission was [funded by Google.org. Model development was](funded by Google.org. Model development was) funded by [Google.org](Google.org) and Mastercard Center for Inclusive Growth. Interventions at John Jay College were supported by Gray Foundation.  \nCorrespondence concerning this article should be addressed to John Harnisher, email: [john.harnisher@datakind.org](john.harnisher@datakind.org)  \nPublished by Digital Commons @ West Chester University, 2024 1  \nCOLLABORATIVE ALGORITHMS FOR STUDENT SUCCESS 2  \nAbstract  \nImproving graduation rates is a crucial goal in higher education, but understanding who is at risk of dropping out is resource intensive, creating barriers to effective retention interventions. Thus,  \nidentifying factors that predict drop out risk is increasingly of interest, yet current approaches  \noften lack transparency and accessibility. To aid in this effort, DataKind collaborated with John  \nJay College of Criminal Justice (JJC) to co-create a data-assisted student success tool, utilizing  \nmachine learning to identify students at risk of drop out and centering the role of advisors in  \nsupporting students. The insights from this tool are used by JJC to provide proactive  \ninterventions (i.e., increased academic counseling) for the students identified as most in need of support. After implementing the data-assisted student success tool and associated inventions, JJC saw a rise in senior graduation rates, suggesting the value collaborative and human-centered data  \nscience tools have in fostering student success.  \nKey Word","cbCaiou4adk2wD0P","https://ap.wps.com/l/cbCaiou4adk2wD0P","pdf",476693,1,19,"English","en",105,"# Abstract\n## Background and problem statement\n## Data-assisted student success tool (collaboration and goals)\n## Outcomes and implications for retention","[{\"question\":\"Why is identifying students at dropout risk important in higher education?\",\"answer\":\"Graduation rates are a key measure of success and affect both students and institutions. Targeted support depends on knowing which students are most at risk rather than applying interventions broadly.\"},{\"question\":\"How did DataKind and John Jay College collaborate to address retention?\",\"answer\":\"They co-created a data-assisted student success tool using machine learning to identify students at dropout risk, while centering advisors to provide support and proactive interventions.\"},{\"question\":\"What results did John Jay College observe after implementing the tool?\",\"answer\":\"After deploying the data-assisted student success tool and related interventions, John Jay College saw a rise in senior graduation rates, indicating the approach supports student success.\"}]","Collaborative Development of Machine Learning Algorithms for Student Success at John Jay College - Article | PDF",1785936749,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"collaborative-development-of-machine-learning-algorithms-for-student-success-at-john-jay-college-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/collaborative-development-of-machine-learning-algorithms-for-student-success-at-john-jay-college-article/127083/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is identifying students at dropout risk important in higher education?","Question",{"text":75,"@type":76},"Graduation rates are a key measure of success and affect both students and institutions. Targeted support depends on knowing which students are most at risk rather than applying interventions broadly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did DataKind and John Jay College collaborate to address retention?",{"text":80,"@type":76},"They co-created a data-assisted student success tool using machine learning to identify students at dropout risk, while centering advisors to provide support and proactive interventions.",{"name":82,"@type":73,"acceptedAnswer":83},"What results did John Jay College observe after implementing the tool?",{"text":84,"@type":76},"After deploying the data-assisted student success tool and related interventions, John Jay College saw a rise in senior graduation rates, indicating the approach supports student success.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]