Defining User Knowledge Level by Using Decision Tree Induction Approach

Abstract

This paper describes the method to classify user’s knowledge level using decision tree induction. Decision trees can easily be converted to classification rules by using decision tree induction. This system is to estimate classifier accuracy that is important to evaluate how accurately a given classifier will label future data. In this paper, we present the classification of training data in which the resulting classifier is a decision tree induction. Decision Tree method for classification is exploited to identify user knowledge level after they proceed to learn lectures. As a result, user can know their knowledge level after learning and they can also test their knowledge level.