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Autor*innen: Boubekki, Ahcène; Bengs, Daniel
Titel: Mining implications from data
Aus: Seidl, Thomas;Hassani, Marwan;Beecks, Christian (Hrsg.): Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen: RWTH, 2014 (CEUR Workshop Proceedings, 1226), S. 205-216
URL: http://ceur-ws.org/Vol-1226/paper32.pdf
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Datenanalyse; Itemanalyse; Struktur; Test; Fragebogen; Algorithmus; Experiment; Methode
Abstract: Item Tree Analysis (ITA) can be used to mine deterministic relationships from noisy data. In the educational domain, it has been
used to infer descriptions of student knowledge from test responses in order to discover the implications between test items, allowing researchers
to gain insight into the structure of the respective knowledge space. Existing approaches to ITA are computationally intense and yield results
of limited accuracy, constraining the use of ITA to small datasets. We
present work in progress towards an improved method that allows for
effcient approximate ITA, enabling the use of ITA on larger data sets.
Experimental results show that our method performs comparably to or
better than existing approaches. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung