Adaptive Speed Tests
Publikation: Beiträge in Sammelwerken › Aufsätze in Sammelwerken › Forschung › begutachtet
Standard
Proceedings of the German Workshop on Knowledge Discovery and Machine Learning. Hrsg. / Andreas Henrich; Hans-Christian Sperker. Bamberg: Lehrstuhl für Medieninformatik - Universität Bamberg, 2014. S. 86-90.
Publikation: Beiträge in Sammelwerken › Aufsätze in Sammelwerken › Forschung › begutachtet
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Adaptive Speed Tests
AU - Bengs, Daniel
AU - Brefeld, Ulf
PY - 2014
Y1 - 2014
N2 - 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 measured by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals. The algorithm iteratively computes a new difficulty setting, such that the amount of belief 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. © LWA 2013 - Lernen, Wissen and Adaptivitat, Workshop Proceedings. All rights reserved.
AB - 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 measured by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals. The algorithm iteratively computes a new difficulty setting, such that the amount of belief 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. © LWA 2013 - Lernen, Wissen and Adaptivitat, Workshop Proceedings. All rights reserved.
KW - Informatics
KW - Adaptives Testen
KW - Empirische Untersuchung
KW - Fähigkeit
KW - Kompetenz
KW - Lernen
KW - Business informatics
M3 - Contributions to collected editions/anthologies
SP - 86
EP - 90
BT - Proceedings of the German Workshop on Knowledge Discovery and Machine Learning
A2 - Henrich, Andreas
A2 - Sperker, Hans-Christian
PB - Lehrstuhl für Medieninformatik - Universität Bamberg
CY - Bamberg
ER -