Abstract
Preventing truancy and expulsion (in this paper, referred to collectively as “dropouts”) is an extremely important task for educational institutions. Individual conference is a realistic measure for preventing such dropouts. However, there are various issues with holding individual conferences, such as the skills of the conference holder and the personnel cost. This paper presents the anomaly detection method as a method for predicting which students will drop out using data provided by an educational support system. This method is expected to reduce the issue of personnel cost and heighten the overall effectiveness of individual conferences.
