Evaluation of data mining algorithms on educational data using multi-criteria decision-making methods

Survey academic performance by educational data mining is one of the most important issues in the field of educational management and researchers focus on it. The purpose of this study is to present an experimental method for appropriate algorithm selection in predicting students' academic status in two and three classes. Two-class database predicts the admission or rejection of students in the course, while the database of the three classes, in addition to admission or rejection, identifies students who are prone and elite. Using the previous articles in the field of educational data mining and experts' opinions, factors that effect on academic performance of students were identified and database was compiled based on them. After optimization of parameters and implementation of different algorithms, the performance scores of the algorithms were calculated using paired t-test based on three indexes include of accuracy, f-measure, and ROC, algorithms were compared by TOPSIS and VIKOR methods. In the two-class mode, Support Vector Machine algorithm in TOPSIS with value of 0.999115 and VIKOR with value of zero has shown the best performance. In the multi-class mode, the Logistic Regression algorithm in TOPSIS and VIKOR in turns with values 0.9986044 and 0.0009798 performances better than other algorithms. The proposed method can be used as a tool for selecting algorithm that has the best pergormance in educational data mining. Because choosing the algorithm to achieve accurate and exact results is very effective and can be taken into account in the process of counseling and preventing students' academic failure