filename     : Kae16a.pdf
entry        : article
conference   : Sixth International Conference on Learning Analytics \& Knowledge, Edinburgh, United Kingdom
pages        : 289-298
year         : 2016
month        : April
title        : When to Stop?: Towards Universal Instructional Policies
subtitle     :
author       : Tanja K\"aser, Severing Klingler, and Markus Gross
booktitle    : Proceedings of Learning Analytics \& Knowledge
ISSN/ISBN    :
editor       : 
publisher    : ACM
publ.place   : New York, NY, USA
volume       : 
issue        :
language     : English
keywords     : individualization, instructional policies, noisy data, student modeling, wheel-spinning
abstract     : The adaptivity of intelligent tutoring systems relies on the accuracy of the student model and the design of the instructional policy. Recently an instructional policy has been presented that is compatible with all common student models. In this work we present the next step towards a universal instructional policy. We introduce a new policy that is applicable to an even wider range of student models including DBNs modeling skill topologies and forgetting. We theoretically and empirically compare our policy to previous policies. Using synthetic and real world data sets we show that our policy can effectively handle wheel-spinning students as well as forgetting across a wide range of student models.