Student Performance Prediction And Analysis

Abstract: The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students� performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. This project is developed to justify the capabilities of students in various subjects. In this, the classification task is used to evaluate students� performance and as there are many approaches that are used for data classification, the decision tree method and probabilistic classification method is used here. By this task we extract knowledge that describes students� performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. In addition to this, we will also compare the results generated by two classification algorithms, namely ID3 and Na�ve Based algorithm, and thereby determine which algorithm is more accurate.

Keywords: Data Set, Data Mining, Classification Task, ID3 Algorithm, Na�ve Based Algorithm.