Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms

Abstract

Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.

Exploring the intricacies of human memory and its analogous representation in ChatGPT

“…This study aims to delve deeper into this analogy, exploring how AI models [2]- [4], specifically ChatGPT, replicate the associative processes of human memory. Despite advancements in AI [5]- [7], challenges persist in enabling machines to mimic the nuanced, context-dependent nature of human memory associations. Our contribution to this field includes a novel examination of the parallels between human memory's associative processes and the response generation mechanisms in ChatGPT.…”

Framework for Design of Security Systems for Monitoring Examinees and Proctors During External Offline Examinations in Nigeria

“…The system allows Invigilators to monitors the exam halls through CCTV live streaming and controls by using ultra-sensitive microphones and speakers. Mahmood et al (2022) implemented an automatic invigilation system to detect unethical activities of students during an examination using deep learning model. The proposed model uses Faster Regional Convolution Neural Network to classify student activities into two categories: cheating; and no cheating.…”