@InProceedings{Supelec721,
author = {Olivier Pietquin and Lucie Daubigney and Matthieu Geist},
title = {{Optimisation of a Tutoring System from a Fixed Set of Data}},
year = {2011},
booktitle = {{Proceedings of the ISCA workshop on Speech and Language Technology in Education (SLaTE 2011)}},
address = {Venice (Italy)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/SLaTE_2011_LDMGOP.pdf},
abstract = {In this paper, we present a general method for optimizing a
tutoring system with a target application in the domain of
second language acquisition. More specifically, the
optimisation process aims at learning the best sequencing
strategy for switching between teaching and evaluation sessions
so as to maximise the increase of knowledge of the learner in
an adapted manner. The most important feature of the proposed
method is that it is able to learn an optimal strategy from a
fixed set of data, collected with a hand-crafted strategy. This
way, no model (neither cognitive or probabilistic) of learners
is required but only observations of their behavior when
interacting with a simple (non-optimal) system. To do so, a
particular batch-mode approximate dynamic programming algorithm
is used, namely the Least Square Policy Iteration algorithm.
Experiments on simulated data provide promising results. }
}