A Comprehensive Examination Assessment Model using Machine Learning

Abstract

The main objective of this work is to develop an application for educational institutes. The app will be foremost required by any students and professors to ease the examination process and experience. Using Item Response Theory (IRT), our system can tailor a unique testing experience for every test-taker based on his ability, while the system evaluates the answer responses ‘on the fly’ using various Natural Language Processing (NLP) techniques and a probabilistic classifier. With the help of this system and human evaluators, a fair analysis of the test-takers ability can be inferred. With the use of Machine Learning algorithms, our proposed system evaluates the answers provided with the utmost accuracy, for assessments computers provide quite a few advantages for both faculties and test-takers. Nonetheless, in literature, there is no agreement on the equality of paper-and-pencil and computer-based test environments. The system expects each subject to be divided into multiple modules. The test-taker will be tested for each module separately. The cumulative scores for each of these individually tested modules will give the score for the subject.