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dc.contributor.authorSchiffner, Daniel
dc.contributor.authorRitter, Marcel
dc.contributor.authorHorn, Florian
dc.contributor.editorKrömker, Detlef
dc.contributor.editorSchroeder, Ulrik
dc.date.accessioned2019-03-28T08:48:35Z
dc.date.available2019-03-28T08:48:35Z
dc.date.issued2018
dc.identifier.isbn978-3-88579-678-7
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21039
dc.description.abstractWe propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDeLFI 2018 - Die 16. E-Learning Fachtagung Informatik
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-284
dc.subjectFiber Bundles
dc.subjectExchange Format
dc.subjectLearning Analytics
dc.subjectPython
dc.subjectxAPI
dc.titleLearning Analytics Bundleen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages195-206
mci.conference.sessiontitleLearning Analytics
mci.conference.locationFrankfurt am Main
mci.conference.date10.-12. September 2018


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