Stream Processing for Performance Analysis of Identifying Dropout Students utilizing Different Decision Tree Based Methods

Education paves the way for a person to live a secure, prosperous life. In a similar manner, the percentage of a nation’s population with a higher degree of education may have an impact on that nation’s progress. However, this number is decreasing as a result of early school abandonment. Furthermore, if a student cannot continue due to a dropout, the resources of the country are decreased. Despite the fact that the percentage of dropouts is steadily decreasing, it is still quite difficult for educational institutions to detect these students. The improvement of student performance is a school’s top goal, thus it makes sure that all students graduate on time. Student dropout, however, is a substantial obstacle that adversely affects this aim. Finding out what causes dropouts is essential to determining a response. The root factors vary from student to student; some are related to workload and mental toughness. In this work, many decision tree (DT) approaches have been proposed and investigated.