AI-Powered Classification for Cheating Detection in Offline Examinations Using Deep Learning Techniques with CUI Dataset
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Supervising students during examinations is a very demanding process, and real-time supervision by human proctors proves to be challenging. This method is time-consuming and involves the extra work of monitoring several students concurrently. Automating exam activity recognition is perceived as one of the ways to address these challenges. In this work, we designed, implemented, and tested an accurate deep learning-based system that classifies student activities during examinations using a pre-trained ResNet50 model. This new concept helps expedite the rate at which exams are monitored and supervised, minimizing the role of human proctors. An amalgamated dataset was used, and the model works with six types of student behaviors, incorporating dropout layers for better performance. The Adam optimizer was employed for fine-tuning the learning process, and k-fold cross-validation was utilized to ensure the model's robustness. The system achieved a high training accuracy of 96%, with 57% of all the documents producing output close to 1 for all categories, demonstrating high precision rates. The results indicate that the proposed method is reliable for future automated proctoring systems. Supervisors can focus on more critical tasks, such as addressing student concerns, rather than constantly monitoring every student's movement. Moreover, the automation system provides consistent and unbiased supervision, eliminating human errors and fatigue that could otherwise affect the monitoring process. This ensures a fairer examination environment where all students are treated equally under constant vigilance. The system can also be scaled to handle larger examination rooms or remote testing, providing flexibility in its deployment.
Authors and Affiliations
M. Talha Jahangir, Numan Subhani, Sadia Nadeem, Fatima Abid
Keywords
Related Articles
- EP ID EP760547
- DOI -
- Views 23
- Downloads 0
Advanced Blast Algorithm for Molecular Identification, Biodegradation and Decolorization of Synthetic Melanoidins Using Fungal Species Isolated from Soil and Spent Wash
Introduction/Importance of Study: Distillery spent wash contains a high organic load as Melanoidins. It is generated due to the Millard reaction, which produces sugar and amino acids, leading to extensive water and soi...
Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning
Employee turnover is a significant problem in organizations because it comes with productivity and cost implications. This paper focuses on predicting employee turnover using machine learning techniques that incorporat...
Applying Agile Principles and Methods in Industries Outside IT Landscape: A Systematic Literature Review
Agile methods have become increasingly popular in various industries because of many benefits over the common waterfall like methodologies. Whereas, there are still many issues and uncertainties faced by co-workers, or...
A Systematic Review of Desertification Identification with Multispectral LANDSAT Image and Deep Learning Models
The use of multispectral Landsat images and deep learning models for desertification detection has been reviewed in this research. The role of deep learning models is found to significantly increase the identification...
A Large Language Model-based Web Application for Contextual Document Conversation
The emergence of Large Language Models (LLM), such as ChatGPT, Gemini, and Claude has ushered in a new era of natural language processing, enabling rich textual interactions with computers. However, despite the capab...