Paper Key : IRJ************764
Author: Vaibhav Gawali,Nishant Patil,Ritesh Deshmukh,Ramdas Pawade,Dr.raise Khan
Date Published: 08 Nov 2024
Abstract
In today's world, online education has become very essential because of the fast growth in technology. Across the globe, as schools were shut down to contain the COVID-19 virus spread quickly led realization of the need for e-learning mediums too. While teaching in this rapid shift, the role of Artificial Intelligence (AI) comes into play to give academic integrity as an integral feature especially when we come for online examinations that make one question about how not every student is cheating! Students taking online exams often try to cheat by referring to books, asking people in the room for answers, etc.This can be implemented using AI tools and algorithms like a Local Binary Pattern Histogram, Dlib toolkit, and OpenCV library for facial detection and recognition, which offer prior experience in building smart invigilation systems. The aim of this system is to simplify examination processes by validating student identity and minimizing cases of impersonation or fraud.One of the major obstacles in this regard is assessing subjective answers, which determines whether a student has understood and remembered that information to briefly express it. Where objective questions have fixed answers but subjective ones may be corrected differently. Grading these responses manually can be very time-consuming and automating this process is not always straightforward. To address this, our system leverages the power of machine learning (ML) and natural language processing (NLP), to automatically grade subjective answers by matching a students response with an ideal answer provided by the exam creator.
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