Assistive tool for the Evaluation of online Exam Papers in Tertiary Education

Abstract

Digital education or online education is a necessity of the age for all students and educators. Of these, the online exams occupy a leading position. Because both parties finally face that experience. According to current difficult world situation (especially covid 19) online exams help for educators and students to continue their education. That situation students may face to a successful online exam paper and teachers must be prepared standard online exam paper. I considered what the best methods are and what are the draw backs of current online examination systems in tertiary level. Also, I have researched how to evaluate online exam paper in those platforms. I have discussed with exam platform developers and collect information. After collecting these data then analyze and make report for the final thesis. This project’s main aim is to automate the evaluation process of the online exam paper. Here I reveal the bloom’s revised taxonomy level of each question and generate report. Then find how many percentages covered by the online exam paper of related course unit. This platform allows for educators to login and prepare the question paper and evaluate it. System will provide report and educator can analyze it. I use Bloom’s Revised Taxonomy model to evaluation process. Further I applied Natural Language Processing technology for reveal the percentage of course unit that covered by the exam paper. I used python, Django, HTML, CSS and JavaScript as other technologies.