Random Multiple Choice Questions Generation using Nlp
Ch Hemanth1, M Venkat2, B Chitti Babu3, U Rochasvi4, P Daanesh5
1Ch Hemanth, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
2M Venkat, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
3B Chitti Babu, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
4U Rochasvi, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
5P Daanesh, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
Manuscript received on June 20, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 10, 2020. | PP: 395-397 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7104079920 | DOI: 10.35940/ijitee.I7104.079920
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Abstract: Students’ life is incomplete without exams because exams are those that help students in evaluating themselves and thus proceeding further in studies. So, the starting step in conducting such examinations is creating a question paper. Generating a question paper is still in its traditional way, where lecturers or professors that are the teaching staff are doing it manually and wasting a terrible amount of time in selecting what type of questions are to be generated. It’s so difficult to create a question paper as it includes a lot of resource utilization and exhaustion. These tasks can be automated. As we are seeing a lot of development in new, exciting technologies and these technologies can help and can make the process of automation easier. So for automation, we use Machine Learning and Natural Language Processing as this whole task involves using and manipulating textual data. In this solution, we provide our model with a textual paragraph from which the questions are to be selectively generated and we develop the multiple choices using a certain distinctive process for the users.
Keywords: Answers evaluation, MCQ’s generation, Natural Language Processing, NLTK.
Scope of the Article: Natural Language Processing