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Autor*innen: Bengs, Daniel; Brefeld, Ulf
Titel: Adaptive speed tests
Aus: KDML (Hrsg.): Proceedings of the German Workshop on Knowledge Discovery and Machine Learning (KDML 2013), Bamberg: KDML, 2013 , S. 1-4
URL: http://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/speedtest.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Adaptives Testen; Empirische Untersuchung; Fähigkeit; Kompetenz; Lernen
Abstract: The assessment of a person's traits such as ability
is a fundamental problem in human sciences. We focus on assessments of traits that can be mea-
sured by determining the shortest time limit al- lowing a testee to solve simple repetitive tasks,
so-called speed tests. Existing approaches for ad- justing the time limit are either intrinsically
non- adaptive or lack theoretical foundation. By con- trast, we propose a mathematically sound
frame- work in which latent competency skills are rep- resented by belief distributions on compact
inter- vals. The algorithm iteratively computes a new difficulty setting, such that the amount of
be- lief that can be updated after feedback has been received is maximized. We provide theoretical
analyses and show empirically that our method performs equally well or better than state of the art
baselines in a near-realistic scenario.
DIPF-Abteilung: Informationszentrum Bildung