Inference of Naïve Baye's technique on student assessment data

Abstract

The main objective of any educational institute is to provide quality education to students and produce qualified students to the community. This can be achieved only when the institutions are capable of predicting the student's behavior, their attitude towards studies and also the outcome of their result in the forth coming examinations. This can be achieved through various data mining techniques like classification, clustering and rule based mining. Classification techniques like decision trees, Bayesian network and neural networks can be used to predict the student's outcome in the examination based on their attendance percentage, their marks in the internal examination, the historical data available in the form of their previously scored percentage etc. Bayesian classification technique is used to predict the student's outcome in the university examination based on the marks obtained by them in the internal examination. Bayes classification is used to predict the result of the student on an individual basis which has helped the tutor to identify the weak students in each subject. This result has helped the tutors to concentrate on those weak students and bring out better results. This prediction will also help the institution to reduce the drop out ratio and produce better results. © 2012 Springer-Verlag.