Accurately predicting student performance is useful in different contexts in universities. Educational data mining (EDM) is an emerging discipline, concerned with various approaches such as predicting student performance, Analysis and data visualization, providing feedback for supporting instructors, recommendations for students and so on that automatically extracts meaning from large data generated by or related to people's learning activities in an educational setting. For example, identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. One of the biggest challenges is to improve the quality of the educational processes so as to enhance student’s performance. The results of these studies give insight into techniques for accurately predicting student performance.