부산대학교 도서관

In this rapidly changing information age, big data analysis is trying to find the rule of the world. With data mining, big data is able to analyze big or small things in different levels of lives. This research is focus on the application of data mining in education. Most researches on the exploration of education data have implications in predicting the learning effectiveness, which can predict students' mid-term grades, final grades and semester grades with the big data analysis. The exploration of education data has been used by many research to apply the existing predictive model, thus can predict students who failed the semester exam. When there are some students who have a tendency, teacher can take care of their learning conditions earlier, find out the reason why they are poor in the performance, which can improve the quality of student learning. This study extends JAVA automatic grading system that our laboratory developed, increases students' operation behaviors and forecasting analysis models on the system. By using the four algorithms: 'Random Forest', 'Supporting Vector Machine', 'Artificial Neural Networks' and 'Naive Bayes' to predict and analyze the student's drop point of semester grades. According to the research result, the success rate of the 'Random Forest' algorithm is about 67%, and it's better than the other algorithms. The result of the study analysis can help teachers to early tutor students and improve their quality of teaching. Moreover, it plays an important role in the reference value.