Factor Analysis with Data Mining Technique in Higher Educational Student Drop Out

The increase of students’ drop out rate in higher education is one of the important problems in most institutions. The discovery of hidden knowledge from the educational data system by the effective process of data mining technology to analyze factors affecting student drop out can lead to a better academic planning and management to reduce students drop out rate, as well as can inform valuable information for decision making of steak holder to improve the quality of higher educational system. In this paper, we consider three issues of factors affecting students’ drop out rate. These factors are conditions related to the students before admission, factors related to the students during the study periods in the university, and all factors including the target value to be predict for factors analysis. We use tree-based classification algorithm, J48 or C4.5, and Naïve Bayes to analyze the data. To evaluated the model, we use both 10-fold cross validation and supplied test set methods. Accuracy rate was satisfactory and the induced models are actionable and potentially applicable to higher education planning. Key-Words:Higher education, Student drop out, Data mining technique, Classification.