Fairness Assessment of Question Paper using Artificial Intelligent Techniques

Abstract

Examination performance is the only measure of competence in the existing education system of India. Question paper is the primary tool used in an examination, the quality of question paper plays an important role in a student’s future. Hence, any individual should take utmost care while framing the question paper. However, setting up a good question paper for assessment is not a straightforward task, particularly when students come from different backgrounds and intellect. So parameters which need to pay attention are fairness, consistency, novelty and elimination of bias while selecting questions in the question paper. This paper explores the possibility of using knowledge graph technology and deep learning. Knowledge graph in particular, addressing syllabus fairness and deep learning for novelty check. The objective is to propose a feasible framework, taking into consideration several aspects. These aspects are: to meet at least three levels of bloom taxonomy, support of course outcomes (CO) and Program Outcomes (PO) attainment. They are achieved by judging syllabus fairness, support for judging more spontaneous answering potential with novelty checks. The prototype implemented using this framework showed promising results motivating for further research.