Intelligent item assigning for classified learners in ITS using Item Response Theory and Point Biserial Correlation

Computer based assessment is a prevalent method of administering the tests in the education environment. Randomization of test items here may produce unfair effect on test takers which is unproductive in the outcome of the test. Hence, the Intelligent Tutoring System came in to existence which assigns intelligent question depending on the student´s response in the testing session. It will be more productive when the questions are assigned based on the learner ability in the early stage itself. So, this study focus on building up a framework to automatically assign intelligent question based on the learner ability not only all through the session, but also while entry. The learners are classified based on their level by Decision Tree using ID3 algorithm. At end, automatic up gradation of the question levels can be done dynamically. The questions are classified efficiently based on item difficulty using Item Response Theory. Item discrimination is found based on Point Biserial Correlation Coefficient and there by only the items which can discriminate the high and low performers alone are accumulated in the item pool to have maximum effect of intelligence in tutoring system. Thereby the objective of Intelligent Tutoring System is achieved by using both adaptability and intelligence in testing.