Forecasting Students' Grades Using Bayesian Network Models and an Evaluation of Their Usefulness

Abstract

The purposes of this study are: to utilize students' data to forecast their future grades; and to identify the students who would require academic counseling. To achieve these purposes, we propose Bayesian network models as a forecasting method. A Bayesian network is a graphical model that presents the dependence relationship among some variables in a graph structure. By calculating the probability value using the models, it is possible to make forecasts. Moreover, in this study, to facilitate the construction of the Bayesian network models, we introduce data mining. Data mining is the process of discovering new patterns from large amounts of data. In this paper, we use information gain and a decision tree as the data mining methods.