@incollection{Iglesias2004,
abstract = {One of the most important issues in educational systems is to define effective teaching policies according to the students learning characteristics. This paper proposes to use the Reinforcement Learning (RL) model in order for the system to learn automatically sequence of contents to be shown to the student, based only in interactions with other students, like human tutors do. An initial clustering of the students according to their learning characteristics is proposed in order the system adapts better to each student. Experiments show convergence to optimal teaching tactics for different clusters of simulated students, concluding that the convergence is faster when the system tactics have been previously initialised. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.},
author = {Iglesias, Ana and Mart?nez, Paloma and Aler, Ricardo and Fern?ndez, Fernando},
booktitle = {Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)},
doi = {10.1007/978-3-540-30215-5_34},
file = {:home/fernando/papers/tmp/10.1007{\%}2F978-3-540-30215-5{\_}34.pdf:pdf},
issn = {03029743},
pages = {454--463},
title = {{Learning Content Sequencing in an Educational Environment According to Student Needs}},
url = {https://link.springer.com/chapter/10.1007{\%}2F978-3-540-30215-5{\_}34 http://link.springer.com/10.1007/978-3-540-30215-5{\_}34},
volume = {3244},
year = {2004}
}

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