Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aboudihaj, Rihab
Unknown Affiliation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Enhancing convolutional neural network based model for cheating at online examinations detection Ouahabi, Sara; Aboudihaj, Rihab; Sael, Nawal; El Guemmat, Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp843-852

Abstract

In the last few years, e-learning has revolutioning education, giving students access to diverse and adaptable on-line resources, but it has also face a major challenge: cheating on online exams. Students now use variant cheating methods include consulting unauthorized documents, communicating with others during the exam, searching for information on the internet. Combating these cheating practices has become crucial to preserving the integrity of academic assessments. In this context, artificial intelligence (AI) has emerged as an essential tool for mitigating this fraudulent behavior. Equipped with advanced machine learning capabilities, AI can examine a wide range of data to detect student suspicious behavior. This study develops an approach based on a convolutional neural network (CNN) model designed to detect cheating by analyzing candidates' head movements during online exams. By exploiting the FEI dataset, this model achieves an interesting accuracy of 97.28%. In addition, we compare this model to the well-known transfer learning models used in the literature namely, ResNet50, VGG16, DenseNet21, MobileNetV2, and EfficientNetB0 demonstrating the out performance of our approach in detecting cheating during online exams.
Co-Authors El Guemmat, Kamal Ouahabi, Sara Sael, Nawal