Discrete model based answer script evaluation using decision tree rule classifier

Student answer script contains different type of answers to be evaluated. The answers may be of objective type answers, subjective type answers, mathematical answers, diagrammatic answers, classification type answers, each of which may require unique approaches for automated evaluation. Classification type answer makes a student to map an answer/object to a particular class/type, like types of grammars, types of normal forms and types of functions. This paper proposes an approach to automate the classification type answers using discretemodel which classifies the types of relations in discrete mathematics domain. The proposed approach achieves 100% classification accuracy when compared with other types of classification since the pellet reasoner is used as a classifier which uses predefined classification rules to classify the types of relations.