A Method for Tuning the Structure of a Hierarchical Causal Network Used to Evaluate a Learner's Profile

Yoshitaka FUJIWARA; Yoshiaki OHNISHI; Hideki YOSHIDA

doi:10.1093/ietisy/e89-d.7.2310

A Method for Tuning the Structure of a Hierarchical Causal Network Used to Evaluate a Learner's Profile

Yoshitaka FUJIWARA, Yoshiaki OHNISHI, Hideki YOSHIDA

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This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2310-2314

Publication Date: 2006/07/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.7.2310

Type of Manuscript: LETTER

Category: Educational Technology

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Yoshitaka FUJIWARA, Yoshiaki OHNISHI, Hideki YOSHIDA, "A Method for Tuning the Structure of a Hierarchical Causal Network Used to Evaluate a Learner's Profile" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 7, pp. 2310-2314, July 2006, doi: 10.1093/ietisy/e89-d.7.2310.
Abstract: This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2310/_p

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@ARTICLE{e89-d_7_2310,
author={Yoshitaka FUJIWARA, Yoshiaki OHNISHI, Hideki YOSHIDA, },
journal={IEICE TRANSACTIONS on Information},
title={A Method for Tuning the Structure of a Hierarchical Causal Network Used to Evaluate a Learner's Profile},
year={2006},
volume={E89-D},
number={7},
pages={2310-2314},
abstract={This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2310},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - A Method for Tuning the Structure of a Hierarchical Causal Network Used to Evaluate a Learner's Profile
T2 - IEICE TRANSACTIONS on Information
SP - 2310
EP - 2314
AU - Yoshitaka FUJIWARA
AU - Yoshiaki OHNISHI
AU - Hideki YOSHIDA
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2310
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.
ER -