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Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms.

Fatima Mahmood1, Jehangir Arshad2, Mohamed Tahar Ben Othman3

  • 1Computer Engineering Department, University of Engineering and Technology Lahore, Lahore 54000, Pakistan.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated invigilation system using deep learning to detect and prevent cheating during real-time examinations. The model accurately identifies suspicious student activities, enhancing exam integrity.

Keywords:
Convolution Neural Network (CNN)Discriminative Deep Belief Network (DDBN)Multi-Task Cascaded Convolutional Neural Networks (MTCNN)Regional Convolution Neural Network (RCNN)Regional Proposal Network (RPN)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Educational Technology

Background:

  • Examination cheating undermines academic integrity and fairness.
  • Traditional exam supervision methods are limited by human capacity and prone to errors.
  • Automated systems offer a scalable solution to monitor large student groups effectively.

Purpose of the Study:

  • To develop a deep learning-based model for real-time detection and control of unethical activities during examinations.
  • To enhance the accuracy and efficiency of exam supervision compared to traditional methods.
  • To provide a robust solution for maintaining academic integrity in educational institutions.

Main Methods:

  • Utilized Faster Regional Convolution Neural Network (Faster RCNN) for detecting suspicious student activities based on head movements.
  • Employed Multi-task Cascaded Convolutional Neural Networks (MTCNN) for accurate face detection and student identification.
  • Trained and tested the model on various real-time examination scenarios.

Main Results:

  • Achieved high accuracy rates: 99.5% for training and 98.5% for testing.
  • Demonstrated efficiency in monitoring over 100 students simultaneously within a single frame.
  • Validated the model's performance across diverse real-time examination settings.

Conclusions:

  • The proposed Automatic Invigilation System effectively detects and monitors suspicious student behaviors.
  • Implementation in educational settings can significantly reduce and prevent examination cheating.
  • This AI-driven approach offers a reliable method for upholding academic honesty.