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In a narrow sense, EDS uses analytical and computational methods for education-related questions; in a broader sense, it functions as an umbrella for new techniques that generate data forms, measures, descriptions, predictions, and experiments. The overview defines the field and summarizes 12 articles, highlighting promises for education and directions for future scholar focus.",{"@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/education-data-science-past-present-future/590124/",{"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/education-data-science-past-present-future/590124.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does Education Data Science (EDS) mean in the article?","Question",{"text":112,"@type":113},"EDS is described both narrowly and broadly: narrowly as applying statistics and computational techniques to educational questions, and broadly as a umbrella for computational methods that create new data, measures, and experiments in education.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the article explain the historical development of data science?",{"text":117,"@type":113},"It traces early precursors in the 1960s, cites work framing “data science” as an alias for computer science and statistics, and notes institutions and workshops such as IASC and KDD/SIGKDD, culminating in later recognition of data science as an emerging discipline.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the rise of EDS in education research identified?",{"text":121,"@type":113},"By examining ERIC publication corpus adoption of key terms, including Learning Analytics, Machine Learning, Artificial Intelligence, Data Science, and Natural Language Processing, and comparing their growth with overall education research trends.","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},590124,1792191590,{"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":24,"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":41,"preview_page_count":4},1374404737137,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","AERA Open  \nJanuary-December 2021, Vol. 7, No. 1, pp. 1–12 DOI: 10. 1177/23328584211052055  \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)  \nEducation Data Science: Past, Present, Future  \nDaniel A. McFarland  \nSaurabh Khanna  \nBenjamin W. Domingue  \nStanford University  \nZachary A. Pardos  \nUniversity of California, Berkeley  \nThis AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries fromEDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate anAERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.  \nKeywords: data science, network analysis, natural language processing, machine learning, learning analytics, data mining  \nPast: How Did We Get Here? Why Data Science Now?  \nHistory of Data Science  \nAs early as the 1960s, precursors of data science began toemerge. Tukey’s “The Future of Data Analysis” can be considered a first step in this direction. His references to exploratory and confirmatory data analyses set in motion a chain of events that saw Naur (1974) and Wu (1986) arguing for“data science” as an alias for computer science and statistics respectively (Donoho, 2017) . Naur (1974) published the“Concise Survey of Computer Methods” that surveyed data processing methods across a wide variety of applications. He considered data science to be “the science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields [emphasis added] and sciences”(p. 30) . Despite this allusion to “other” fields, data science originated as a byproduct of work in STEM fields and especially computer science and statistics. The International Association for Statistical Computing (IASC) in 1977 was established with a “mission to link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.” The first Knowledge Discovery in Databases (KDD) workshop was organized in 1989, which later became the annual ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) . The 1992 statistics symposium at the University of Montpellier was one of the first acknowledgments of data science as an emerging discipline harnessing  \ndata in different structural forms (Escoufier et al., 1995) . Furthermore, data science was first featured as a standalone topic at the 1996 International Federation of Classification Societies conference (L. Cao, 2017) .  \nApproaching the present, data science has become an essential idea not limited by traditional disciplinary boundaries. This need for boundary-crossing is exemplified by an argument to expand statistics beyond mere theoretical arguments (Cleveland, 2001) . As the popularity of data science grew with the dawn of a new century, both the Data Science Journal and the Journal of Data Science were launched by the Committee on Data for Science and Technology and Columbia University, respectively. The establishment of these journals has been one part of a process leading to the consideration of data science as a domain distinct from both computer science and statistics. There were several justi","cbCaioMFngZKkdvl","https://ap.wps.com/l/cbCaioMFngZKkdvl","pdf",595472,12,"English","# Education Data Science: Past, Present, Future\n## Past: How Did We Get Here? Why Data Science Now?\n## History of Data Science\n## The Emergence of Education Data Science","[{\"question\":\"What does Education Data Science (EDS) mean in the article?\",\"answer\":\"EDS is described both narrowly and broadly: narrowly as applying statistics and computational techniques to educational questions, and broadly as a umbrella for computational methods that create new data, measures, and experiments in education.\"},{\"question\":\"How did the article explain the historical development of data science?\",\"answer\":\"It traces early precursors in the 1960s, cites work framing “data science” as an alias for computer science and statistics, and notes institutions and workshops such as IASC and KDD/SIGKDD, culminating in later recognition of data science as an emerging discipline.\"},{\"question\":\"How is the rise of EDS in education research identified?\",\"answer\":\"By examining ERIC publication corpus adoption of key terms, including Learning Analytics, Machine Learning, Artificial Intelligence, Data Science, and Natural Language Processing, and comparing their growth with overall education research trends.\"}]","Education Data Science: Past, Present, Future | PDF",1792068850]