A probabilistic student model is proposed that is suitable for representing the uncertainty regarding the estimate of the student's knowledge of Euclidean geometry problem solving procedures. The probabilistic student model is based on the use of Bayesian belief networks. Several efficient computer-assisted procedures are presented that allow both the automated assessment of the Bayesian belief network's topological structure and also the automated computation of the conditional probability matrices. Numerical results (derived from real-world experiments) are also reported that prove the effectiveness of the use of these automated procedures in the process of construction of the student model. © 1998 Taylor & Francis Group, LLC.
Roccetti M., Salomoni P. (1998). Using bayesian belief networks for the automated assessment of students' knowledge of geometry problem solving procedures. JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 10(2), 145-178 [10.1080/095281398146815].
Using bayesian belief networks for the automated assessment of students' knowledge of geometry problem solving procedures
Roccetti M.;Salomoni P.
1998
Abstract
A probabilistic student model is proposed that is suitable for representing the uncertainty regarding the estimate of the student's knowledge of Euclidean geometry problem solving procedures. The probabilistic student model is based on the use of Bayesian belief networks. Several efficient computer-assisted procedures are presented that allow both the automated assessment of the Bayesian belief network's topological structure and also the automated computation of the conditional probability matrices. Numerical results (derived from real-world experiments) are also reported that prove the effectiveness of the use of these automated procedures in the process of construction of the student model. © 1998 Taylor & Francis Group, LLC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
