Intelligent Automated Exam System with Adaptive Question Bank and multi-Bloom Taxonomy Assessment

Abstract

Most universities use manual examination systems, whereby the lecturer spends many hours to write exam sets for number of subjects, depends on how many subjects the lecturer has, beside preparing exam questions, the lecture must prepare answer scheme for each exam paper, this process is time consuming. After that, the lecturer must verify with a verifier which makes the process longer and still has no guarantee to minimize errors. Lecturers spend long hours marking these answer scripts. This process compromises the quality and talents of lecturers which could be used for better purposes such as research and modules improvements. The development of the proposed system utilizes a cumulative question bank that aggregates questions from question bank associated to it and updates continuously. The multi bloom level selection feature enables the system to categorize questions based on bloom’s taxonomy and allow proper evaluation of student’s cognitive skills. This system aims to improve effectiveness, efficiency and fairness of the examination process. The methodology applied for this research was survey and experiment methods. The survey consisted of two phases. The first phase was to get feedback of lectures of using the current examination system, the second phase was after implementing the system to get feedback of lectures after applying the new system to get the successful rate of the proposed system.