dc.contributor.author |
Pesonen, Joonas |
|
dc.contributor.author |
Fomkin, Anna |
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dc.contributor.author |
Jokipii, Lauri |
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dc.date.accessioned |
2019-02-07T08:07:01Z |
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dc.date.available |
2019-02-07T08:07:01Z |
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dc.date.issued |
2018-06-28 |
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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 > |
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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 |
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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 |
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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 |
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dc.language.iso |
eng |
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dc.relation.ispartof |
|
|
dc.rights.uri |
info:eu-repo/semantics/openAccess |
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dc.subject |
113 Computer and information sciences |
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dc.title |
Building Data Science Capabilities into University Data Warehouse to Predict Graduation |
en |
dc.type |
Abstract |
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dc.contributor.organization |
Doctoral Programme in Cognition, Learning, Instruction and Communication |
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dc.contributor.organization |
Department of Education |
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dc.description.reviewstatus |
Peer reviewed |
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dc.rights.accesslevel |
openAccess |
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dc.type.version |
acceptedVersion |
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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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