Survey on Students ’ Academic Failure and Dropout using Data Mining Techniques

Educational data mining is in the habit of learn the data available in the field of education and show up the hidden knowledge from it. Classification methods like decision trees, machine learning, rule mining, etc can be applied on the educational data for forecasting the student’s behavior, performance of them in examination etc. This prediction will well helpful for tutors to classify the weak students and help them to score improved marks. The classification approach is applied on student’s internal assessment data to predict their performance in the final exam. The result of the classification categorized the number of students who are to be expected to fail or pass. The outcome result is given to the tutor and steps were taken to improve the performance of the students who were predicted as fail in the examination. After the statement of the results in the final examination the marks acquired by the students are provide into the system and the results were investigated. The proportional analysis results states that the prediction has helped the weaker students to improve and brought out betterment in the result. The algorithm is also analyzed by duplicating the same data and the result of the duplication brings no much change in predicting the student’s outcome. The goal of this survey is presented the several data mining techniques in determining of student failure. This article provides a review of the available literature on Educational Data mining, Classification method and different feature selection techniques that we should apply on Student dataset