Hirotaka Itoh

Forecasting Students’ Future Academic Records Using Past Attendance Recording Data and Grade Data☆

Abstract In this study, the authors forecast students’ future academic records using past attendance recording data and grade data. We use a Bayesian network as forecasting method. During construction of the Bayesian network forecasting model, unnecessary variables become noise and so lower the forecasting accuracy. Therefore, to improve the forecasting accuracy, we used information gain to reduce the number of variables in the model. As a result, accuracy improved.

A method of identifying students who require guidance using Bayesian network

The presence of students repeating years and withdrawing from university is a concern at most universities. Our research is intended to use student data accumulated through past to support academic guidance, in order to resolve this issue. It is hoped that numbers of students repeating years and withdrawing students will decrease in the future through defining students at high risk of repeating years and withdrawing as students requiring guidance, identifying such students early, and concentrating guidance efforts on them. In this paper, we use a Bayesian network to construct a model for identifying students who require guidance.

Heterogeneous Multi-agents Learning Using Genetic Network Programming with Immune Adjustment Mechanism

A heterogeneous multi-agent system is a system that involves two or more agents that cooperate in order to accomplish a certain task. Genetic Network Programming (GNP) is a technique to automatically build a multi-agent system. In the past, the authors proposed the use of the Immune evolved Genetic Network Programming (IGNP) technique for the automatic construction of multi-agent systems. In this paper, the authors propose the use of Genetic Network Programming with Immune Adjustment Mechanism (GNPIAM) as a technique to automatically build a heterogeneous multi-agent system. In this study, the authors carry out experiments using tile world to evaluate the validity of the proposed method and compare the three techniques—GNP, IGNP, and GNPIAM.