Artificial Neural Network Application to Prediction and Analysis of Students’ Deviant Behavior and Discipline

• Main Authors: Han-Chiang Chao, 趙漢彊
• Other Authors: Shi-Jer Lou
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/23767809514247333901

碩士 === 國立屏東科技大學 === 技術及職業教育研究所 === 96 === The purpose of this research is to build an artificial neural network model of students’ counseling, and we apply it to process prediction and analysis of the estimates of students’ deviant behavior and the punishment for their deviant behavior. According to the result of the prediction, we discuss the causes of students’ deviant behavior and the ways of punishment for their deviant behavior. We could also provide the data for institutions, counselors or teachers as reference materials for counseling. In this study, there were 200 students counseled by the counselors in Military training office and Counselors’ office at one of private vocational senior high schools in Kaohsiung City during the school year of 90 to 93. We divided them into two groups, 120 people in the training group and 80 people in the testing group. The input and output layer consists of 17 inputs which are departments, marriage status of parents, economics, religions, smoking, fighting, being late for school, cheating on tests, counseling, demerits, academic probation, transferring to another school, quitting school, four factors of individuals, families, schools, and societies. Based on our reservation on the outcome of the prediction of artificial network models of the research, we could generalize four causes which are departments students attended, marriage status of parents, religions of students, and home economics conditions. As far as the deviant behavior of students, the causes of deviant behavior of students, and the ways of punishment for the deviant behavior of students are concerned, they vary according to these four causes. The result of the experiment was that we obtained over 80 percent of the accuracy of the prediction, and it shows the feasibility and the practicality of this model.