本研究將機器學習方法套用到學生是否轉學或轉系上，利用南部某私立大學的資訊學院的資料進行研究，透過Decision Tree、SVM(Support Vector Machine)和Logistic Regression的基本框架下，來建立學生轉學或轉系之模型。透過模型所歸納出來的法則，提供給管理者擬定方針，針對欲要轉學或轉系的學生進行輔導或改善教學品質。本研究希望能夠降低轉學或是轉系的發生率。

Currently, universities in Taiwan are facing many management pressures on the insufficient enrollment due to the low birth rate. To make the school continuously operate and to enhance the competitiveness, the universities have to avoid the loss of students. In this study, we propose the student transfer problem meaning the loss of students who switch from one university to another. We use machine learning techniques to address the problem. We study different machine learning methods such as decision tree, support vector machine (SVM) and Logistic Regression, and use them to construct a student transfer prediction model. The prediction model can be used to predict whether a student will switch from one university to another. Our results show that the student transfer mode built by the decision tree has the best prediction capability. We also use our prediction model to infer several strategies that university administrative chiefs can use them to cope with the student transfer problem.

一、中文文獻
吳碧娥，2014，“私校悲歌！105年大學將掀起少子化倒閉潮”，北美智權報,，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Editorial/publish-193.htm

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