[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124738-en":3,"doc-seo-124738-105":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},124738,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Reimagining the machine learning life cycle to improve educational outcomes of students - Research article","Machine learning (ML) is increasingly used in education, from predicting student dropout to supporting university admissions and MOOCs, yet concerns remain about data quality, relevance, and fairness. Using qualitative interviews with education experts and evaluations of ML for Education (ML4Ed) papers from leading applied ML conferences, the study critically examines alignment between education and societal objectives and the technical ML problems, approaches, and interpretations used. Results identify a cross-disciplinary gap in ML problem formulation from education goals and in translating predictions into interventions.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nReimagining the machine learning life cycle to improve educational outcomes of students.  \nPermalink  \n[https://escholarship.org/uc/item/03k1x0d8](https://escholarship.org/uc/item/03k1x0d8)  \nJournal  \nProceedings of the National Academy of Sciences of the United States of America, 120(9)  \nISSN  \n0027-8424  \nAuthors  \nLiu, Lydia T  \nWang, Serena Britton, Tolaniet al.  \nPublication Date  \n2023-02-01  \nDOI  \n10.1073/pnas.2204781120  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nRESEARCH ARTICLE  \nSOCIAL SCIENCES COMPUTER SCIENCES  \n OPEN ACCESS  \nReimagining the machine learning life cycle to improve educational outcomes of students  \nLydia T. Liua,1,2 ID , Serena Wanga,1,2, Tolani Brittonb,1, and Rediet Abebec,1  \nEdited by Jennifer Rexford, Princeton University, Princeton, NJ; received March 22, 2022; accepted September 20, 2022  \nMachine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs) . Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support longstanding education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.  \nmachine learning for social good j problem formulation j education technologies j education interventions j algorithmic fairness  \nThe widespread use of machine learning (ML) across domains remains controversial, with experts exposing concerns around data curation, relevance, and appropriate use of ML techniques as well as the potential for algorithms to create and amplify inequalities. While wide-spread public conversations around the use of ML are a more recent phenomenon, the computer science community has employed ML approaches widely in tasks such as recommendation systems (1) and speech and image recognition (2–4) . More recently, numerous other disciplines have turned toward ML to increase efficiency and improve outcomes. For example, ML algorithms are seeing an increase in use across education. They have been deployed in a variety of ways both at the secondary and postsecondary levels, often with a stated goal of improving student performance. Some of the uses include predicting student dropout at the secondary level (5, 6), evaluating applicants in college and graduate school admissions, and predicting persistence in massive open online courses (MOOCs) (7–9) .  \nExtensive research currently explores the use of machine learning for social good (10–13) or “ML4SG.” Despite a surge in interest in understanding the societal impact of ML, this ","cbCaiaqkdHR1JA0V","https://ap.wps.com/l/cbCaiaqkdHR1JA0V","pdf",907004,1,13,"English","en",105,"# Significance\n## Translational challenges in ML4Ed","[{\"question\":\"What problem does the study address in machine learning for education?\",\"answer\":\"It examines whether the educational and societal objectives stated or implied in ML4Ed papers align with how ML problems are formulated, what objectives are chosen, and how results are interpreted.\"},{\"question\":\"What two gaps in the ML life cycle does the study find?\",\"answer\":\"A cross-disciplinary gap appears when (1) formulating ML problems from education goals and (2) translating ML predictions into real-world interventions.\"},{\"question\":\"How does the study support its conclusions?\",\"answer\":\"It draws on qualitative insights from interviews with education experts and from in-depth evaluations of ML4Ed papers published in prominent applied ML conferences over the past decade.\"}]","Reimagining the machine learning life cycle to improve educational outcomes of students - Research article | PDF",1785894205,33,{"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},"reimagining-the-machine-learning-life-cycle-to-improve-educational-outcomes-of-students-research-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/reimagining-the-machine-learning-life-cycle-to-improve-educational-outcomes-of-students-research-article/124738/",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},"What problem does the study address in machine learning for education?","Question",{"text":75,"@type":76},"It examines whether the educational and societal objectives stated or implied in ML4Ed papers align with how ML problems are formulated, what objectives are chosen, and how results are interpreted.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What two gaps in the ML life cycle does the study find?",{"text":80,"@type":76},"A cross-disciplinary gap appears when (1) formulating ML problems from education goals and (2) translating ML predictions into real-world interventions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study support its conclusions?",{"text":84,"@type":76},"It draws on qualitative insights from interviews with education experts and from in-depth evaluations of ML4Ed papers published in prominent applied ML conferences over the past decade.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]