Investigation of Factors for Student’s Decision in Studying at Phetchabun Rajabhat University by Data Mining Technique
Keywords:data mining, artificial neural network, decision tree, attributes selection, data science
This study presents the use of data mining for investigation of important factors that affect students’ decisions for studying at Phetchabun Rajabhat University. 400 students, including 200 freshmen of Phetchabun Rajabhat University and 200 other freshmen from the other universities, were studied based on questionnaires that had 14 attributes. Score values were collected from the attributes and input data were first analyzed for data classification by ANN and decision tree techniques. 10-fold cross validation and RMSE values were also employed to evaluate classification accuracy. Then, factor identification based on filter ranking method was investigated. Its results were explained by decision tree visualization and IF-THEN rules. The results showed that 400 students were correctly classified into two groups (1) freshmen group from Phetchabun Rajabhat University and (2) freshmen group from the other universities. The classification accuracy of ANN and decision tree was 93.00% and 88.25%, respectively. RMSE (root-mean-square deviation) values were 0.2574 and 0.3031, respectively. Based on filter ranking method, the identified factors affecting students’ decisions were ranked as university brand recognition, family income, and number of majors, university location, parents’ careers and modern curriculum, respectively. From these results, data mining techniques with ANN and decision tree are useful for data processing and they provide meaningful information for university admission system analysis as well as other applications.
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