knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic … success. Different methods and techniques of data mining were compared during the prediction of students' success, applying … Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was …

are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative …

initial study. It is shown that neural networks lead to a significant improvement in the prediction of students at risk …

initial study. It is shown that neural networks lead to a significant improvement in the prediction of students at risk …

dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is … imperative to understand which students are at risk of dropping out and what are the underlying determinants of dropout. We …. The EDS we present is tested and applied on a medium-sized state university with 23,000 students and a medium …

-sampling technique known as the Synthetic Minority Over-Sampling (SMOTE) to improve the accuracy of the students' final grade prediction …

track students. However, there are concerns that the most frequently used college placement exams lack validity and … reliability, and unnecessarily place students from under-represented groups into remedial courses. While recent research has shown … that tracking can have positive effects on student learning, inaccurate placement has consequences: students face …