%0 Conference Paper %1 ChiuWebbKuzmycz97 %A Chiu, B.C. %A Webb, G.I. %A Kuzmycz, M. %B Proceedings of the Sixth International Conference on User Modeling (UM'97) %C New York/Vienna %D 1997 %E Jameson, A. %E Paris, C. %E Tasso, C. %I Springer %K Based Feature Modeling Modeling, User %P 347-358 %T A Comparison of First-Order and Zeroth-Order Induction for Input-Output Agent Modelling %X Most student modelling systems seek to develop a model of the internal operation of the cognitive system. In contrast, Input-Output Agent Modelling (IOAM) models an agent in terms of relationships between the inputs and outputs of the cognitive system. Previous IOAM systems have demonstrated high predictive accuracy in the domain of elementary subtraction. These systems use zeroth-order induction. Many of the predicates used, however, represent relations. This suggests that first-order induction might perform well in this domain. This paper reports a study in which zeroth-order and first-order induction engines were used to build models of student subtraction skills. Comparative evaluation shows that zeroth-order induction performs better than first-order in detecting regularities indicating misconceptions while first-order induction leads zeroth-order in detecting regularities indicating correct concepts and inducing a more comprehensible student model. This suggests there exists a trade-off between these factors and that there is still scope for improvement.

@inproceedings{ChiuWebbKuzmycz97, abstract = {Most student modelling systems seek to develop a model of the internal operation of the cognitive system. In contrast, Input-Output Agent Modelling (IOAM) models an agent in terms of relationships between the inputs and outputs of the cognitive system. Previous IOAM systems have demonstrated high predictive accuracy in the domain of elementary subtraction. These systems use zeroth-order induction. Many of the predicates used, however, represent relations. This suggests that first-order induction might perform well in this domain. This paper reports a study in which zeroth-order and first-order induction engines were used to build models of student subtraction skills. Comparative evaluation shows that zeroth-order induction performs better than first-order in detecting regularities indicating misconceptions while first-order induction leads zeroth-order in detecting regularities indicating correct concepts and inducing a more comprehensible student model. This suggests there exists a trade-off between these factors and that there is still scope for improvement.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {New York/Vienna}, audit-trail = {*}, author = {Chiu, B.C. and Webb, G.I. and Kuzmycz, M.}, biburl = {https://www.bibsonomy.org/bibtex/27f28149be3715045a5db5e9c44a60d04/giwebb}, booktitle = {Proceedings of the Sixth International Conference on User Modeling (UM'97)}, editor = {Jameson, A. and Paris, C. and Tasso, C.}, interhash = {14c100964803da344670e6805be535f2}, intrahash = {7f28149be3715045a5db5e9c44a60d04}, keywords = {Based Feature Modeling Modeling, User}, location = {Chia Laguna, Sardinia}, pages = {347-358}, publisher = {Springer}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {A Comparison of First-Order and Zeroth-Order Induction for Input-Output Agent Modelling}, year = 1997 }