Performance Evaluation in Education System using Sprint Decision Tree classification Algorithm

Abstract

At the present time, the amount of data stored in educational database is increasing rapidly. These databases contain hidden information for improvement of student’s performance. Decision tree is the most useful classification algorithm in educational data mining because of its ease of execution and easier to understand compared to other algorithms. We can get more accurate and valuable results with the help of decision tree algorithm which can be useful for instructors to improve the student learning outcomes. The ID3, C4.5 and CART decision tree algorithms has been applied on the data of students to predict their performance. But all these algorithms are used only for small database. For large database, we are using a new algorithm i.e. SPRINT which removes all the memory restriction and accuracy problem comes in other algorithms. It is fast and scalable than others because it can be implemented in both serial and parallel fashion for good data placement and load balancing. In this work, SPRINT decision tree algorithm is used to solve the problem of classification in education system. Most of the current classification algorithms require that all or a portion of the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. Accuracy and time complexity of SPRINT algorithm is much lesser than other decision tree algorithms.