Optimization Method for Selecting Problems Using the Learner's Model in Intelligent Adaptive Instruction System

Tatsunori MATSUI

Optimization Method for Selecting Problems Using the Learner's Model in Intelligent Adaptive Instruction System

Tatsunori MATSUI

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The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.

Publication: IEICE TRANSACTIONS on Information Vol.E80-D No.2 pp.196-205

Publication Date: 1997/02/25

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Type of Manuscript: Special Section PAPER (Special Issue on Educational System using Multimedia and Communication Technology)

Category: Advanced CAI system using media technologies

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Tatsunori MATSUI, "Optimization Method for Selecting Problems Using the Learner's Model in Intelligent Adaptive Instruction System" in IEICE TRANSACTIONS on Information, vol. E80-D, no. 2, pp. 196-205, February 1997, doi: .
Abstract: The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_2_196/_p

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@ARTICLE{e80-d_2_196,
author={Tatsunori MATSUI, },
journal={IEICE TRANSACTIONS on Information},
title={Optimization Method for Selecting Problems Using the Learner's Model in Intelligent Adaptive Instruction System},
year={1997},
volume={E80-D},
number={2},
pages={196-205},
abstract={The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - Optimization Method for Selecting Problems Using the Learner's Model in Intelligent Adaptive Instruction System
T2 - IEICE TRANSACTIONS on Information
SP - 196
EP - 205
AU - Tatsunori MATSUI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1997
AB - The purpose of our study is to develop an intelligent adaptive instruction system that manages intelligently the learner's estimated knowledge structure and optimizes the selection of problems according to his/her knowledge structures. The system adopts the dynamic problems of high school physics as a material of study, and is intended to operate on a UNIX Work Station. For these purposes, the system is composed of three parts, 1) interface part, 2) problem solving expert part, and 3) optimization expert system part for problem selection. The main feature of our system is that both knowledge structures of learner and teacher are represented by structural graph, and the problem selection process is controlled by the relationship between the learner's knowledge structure and the teacher's knowledge structure. In our system the relationship between these two knowledge structures is handled in the optimization expert system part for problem selection. In this paper the theory of the optimization expert system part for problem selection is described, and the effectiveness of this part is clarified through a simulation experiment of the originally defined matching coefficient.
ER -