Building Data Science Capabilities into University Data Warehouse to Predict Graduation

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dc.contributor.author Pesonen, Joonas
dc.contributor.author Fomkin, Anna
dc.contributor.author Jokipii, Lauri
dc.date.accessioned 2019-02-07T08:07:01Z
dc.date.available 2019-02-07T08:07:01Z
dc.date.issued 2018-06-28
dc.identifier.citation Pesonen , J , Fomkin , A & Jokipii , L 2018 , ' Building Data Science Capabilities into University Data Warehouse to Predict Graduation ' , EUNIS 2018 Annual Congress , Pariisi , France , 06/06/2018 - 08/06/2018 pp. 156-158 . < https://indico.conference4me.psnc.pl/event/160/session/8/contribution/55 >
dc.identifier.citation conference
dc.identifier.other PURE: 122131505
dc.identifier.other PURE UUID: f76099fc-c434-4d5f-b5b7-f78d9aaa3cd9
dc.identifier.other ORCID: /0000-0003-4166-4762/work/53828536
dc.identifier.uri http://hdl.handle.net/10138/298769
dc.description EUNIS 2018 Congress, Tuesday 5 June - Friday 8 June 2018, Centre de Conférences, UPMC, Sorbonne Université, Paris. Proceedings. EUNIS European University Information Systems, Paris, 2018
dc.description.abstract The discipline of data science emerged to combine statistical methods with computing. At Aalto University, Finland, we have taken first steps to bring educational data science as a part of daily operations of Management Information Services. This required changes in IT environment: we enhanced data warehouse infrastructure with a data science lab, where we can read predictive model training data from data warehouse database and use the created predictive models in database queries. We then conducted a data science pilot with an objective to predict students’ graduation probability and time-to-degree with student registry data. Further ethical and legal considerations are needed before using predictions in daily operations of the university. fi
dc.format.extent 3
dc.language.iso eng
dc.relation.ispartof
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Building Data Science Capabilities into University Data Warehouse to Predict Graduation en
dc.type Abstract
dc.contributor.organization Doctoral Programme in Cognition, Learning, Instruction and Communication
dc.contributor.organization Department of Education
dc.description.reviewstatus Peer reviewed
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion
dc.identifier.url https://indico.conference4me.psnc.pl/event/160/session/8/contribution/55
dc.identifier.url http://www.eunis.org/eunis2018/papers/

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