A Proposed Framework for Generating Random Objective Exams using Paragraphs of Electronic Courses

Objective exams (OE) plays a major role in educational assessment as well as in electronic learning. The main problem in the traditional system of exams is a low quality of questions caused by some human factors, such as the traditional method for the development of the exam covers a narrow scope of curriculum topics. This does nothing for the separation of teaching process about the examination process. In this study we present a framework that generates three types of Objective exams questions (multiple choice questions (MCQ), true-false question (T/FQ), and complete Questions (CQ) from paragraphs of electronic course. The proposed framework consists of a lot of main stages, it uses both of the natural language processing (NLP) techniques to generate three types of questions (GFQ, T/FQ, and MCQ), and exam maker (EM), it uses the generated questions to produce the objective exams. The proposed system was evaluated by the extent of its ability to generate multiple objective questions. The questions that have been generated from the proposed system was presented to the three of the arbitrators specialists in the field of computer networks to express an opinion on the extent of their relationship to E-course and the accuracy of linguistic and scientific formulation. The results of the study showed an increase in the accuracy and number of the objective exams that were generated through the proposed system compared to the accuracy and number of the exams created by the traditional system this proves the efficiency of the proposed system. Keywords—Objective exams (OE); Applications Artificial Intelligence (AAI); Random Objective Exams Generation (ROEG)