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Online Exam Portal System Using Machine Learning Algorithm

Authors: Siddhant Chaurasia; Sudhanshu Kumar Singh; Govid Suryakant Shinde; Shubham Shashikant Rasal;

Online Exam Portal System Using Machine Learning Algorithm

Abstract

According to today's requirements, an Online Examination System is critical for educational institutions to organize tests, since it saves time and effort in checking exam papers and preparing result reports. Because the present state of technology makes offline examinations almost unfeasible, online methods should be enhanced to conduct any exams that require evaluation of a candidate's hearing, reading, writing, or speaking abilities. Online Examination Portal with Machine Learning Concepts is a web-based examination system that allows students to take exams online with the purpose of comprehensively evaluating them using a fully automated method that not only saves time but also provides quick and accurate results. This approach assesses a student's listening, reading, speaking, and writing abilities. Exam Portal with Machine Learning Ideas is a Python online application that includes machine learning concepts like as spell checking, language provision evaluation, and exploratory data analysis. This web application keeps track of applicant logins and test attendance in many categories such as multiple choice questions, descriptive questions, audio listening, and so on. Their report and progress will be presented in a graphical and statistical format. The application's major goal is to incorporate machine learning into the examinations to make them more sophisticated and user-friendly. This tool may be used to challenge any form of written exam. This website may also be used by students who are studying for examinations to practice and track their progress.

Keywords

Machine Learning, Web-based examination system, Python

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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