RT Journal Article
T1 Learning Bayesian Networks for Student Modeling
A1 Millán-Valldeperas, Eva
A1 Belmonte-Martínez, María Victoria
A1 Jiménez, Guiomar
A1 Pérez-de-la-Cruz-Molina, José Luis
K1 Enseñanza asistida por ordenador
AB In the last decade, there has been a growing interest in using Bayesian Networks (BN) in the student modelling problem. This increased interest is probably due to the fact that BNs provide a sound methodology for this difficult task. In order to develop a Bayesian student model, it is necessary to define the structure (nodes and links) and the parameters. Usually the structure can be elicited with the help of human experts (teachers), but the difficulty of the problem of parameter specification is widely recognized in this and other domains. In the work presented here we have performed a set of experiments to compare the performance of two Bayesian Student Models, whose parameters have been specified by experts and learnt from data respectively. Results show that both models are able to provide reasonable estimations for knowledge variables in the student model, in spite of the small size of the dataset available for learning the parameters
YR 2015
FD 2015-07-03
LK http://hdl.handle.net/10630/10012
UL http://hdl.handle.net/10630/10012
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026