Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach

Abstract

Exam proctoring is a hectic task i.e., the monitoring of students’ activities becomes difficult for supervisors in the examination rooms. It is a costly approach that requires much labor. Also, it is a difficult task for supervisors to keep an eye on all students at a time. Automatic exam activities recognition is therefore necessitating and a demanding field of research. In this research work, categorization of students’ activities during the exam is performed using a deep learning approach. A new deep CNN architecture with 46 layers is proposed which contains the characteristics of deep AlexNet and SqueezeNet. The model is engineered first with slight modifications in AlexNet. After then, the squeezed branch-like structure of SqueezeNet is grafted/embedded at two locations in the modified AlexNet architecture. The model is named as L2-GraftNet because of dual grafting blocks. The proposed model is first converted to a pre-trained model by performing its training with SoftMax classifier on the CIFAR-100 dataset. Afterwards, the features of the dataset prepared for exam activities categorization are extracted from the above-mentioned pre-trained model. The extracted features are then fed to the atom search optimization (ASO) approach for features optimization. The optimized features are passed to different variants of SVM and KNN classifiers. The best performance results attained are on the Fine KNN classifier with an accuracy of 93.88%. The satisfactory results prove the robustness of the proposed framework. Also, the proposed categorization provides a base for automated exam proctoring without the need for proctors in the exam halls.