A New Model of Automatic and Continuous Online Exam Monitoring

Moukhliss, Ghizlane; Belhadaoui, Hicham; Hilali, Reda Filali

doi:10.1109/syscobiots48768.2019.9028027

Cited by 43 publications

(24 citation statements)

References 18 publications

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“…Educational institutions use the online exam system to improve the quality of education by assessing students' performance in self-paced learning environments. However, despite the importance of the online exam, students engaging in cheating is a widespread phenomenon worldwide (Ghizlane et al, 2019). Therefore, in the field of online exams, several academic researchers have been conducted, including continuous authentication, biometrics methods, face-tracking techniques, and other approaches described below:…”

Section: Literature Reviewmentioning

confidence: 99%

“…(Mahadi et al, 2018) discussed several techniques and suggested combining (facial recognition and keystroke dynamics) could be the best classifiers in the online course for behavioral biometric authentication. Similarly, (Ghizlane et al, 2019) also suggested a combination of smart cards (to check student's identity) and face recognition techniques (for continuous monitoring of a student's webcam) to detect any suspicious behavior during the online exam and avoid any kinds of cheating attempts. (Shdaifat et al, 2020) proposed a model that uses a biometric iris recognition technique in addition to the traditional method of mobile examination login in mobile learning.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

Sharma¹,

Jain²

2022

WEB

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With the dawn of Internet of Things (IoT), interconnected things and smart applications have significantly reduced human intervention in accomplishing tasks. Yet, due to its openness, heterogeneity, limited resources and extensiveness, it suffers from several issues such as lesser capacity links, energy consumption, resource optimization etc. Routing of packets in such constrained environment is yet challenging. Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) protocol is an extended version of AODV protocol. Unlike AODV, LOADng is lighter version which forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ) which is originated from the source. A resource constrained IoT network demands minimal routing control overhead with no packet loss. The optimal selection of routing protocol parameters can improve quality of service (QoS) of such constrained environments. So, in this paper we present optimization of the parameters of LOAD grouting protocol using black widow optimization (BWO) algorithm for IoT. Simulation results depict that the LOADng-BWO outperforms the conventional LOADng protocol in terms of delay, overhead and delivery ratio.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

Sharma¹,

Jain²

2022

WEB

View full text Add to dashboard Cite

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“…are utilized to develop a full fledge online exam management system. For example, Moukhliss Ghizlane et al [13] developed a complete system comprising management and monitoring components where monitoring is accomplished through biometric features and other attributes are used for online exam management. Such studies simultaneously targeting both biometric and software application categories that are placed in General Category.…”

Section: General Categorymentioning

confidence: 99%

“…The distribution of selected studies on the basis of type is shown in Figure 3 where 28% of selected studies are from reputed journals while remaining 72% studies are from No. Scientific Database Studies Total 1 IEEE [49], [36], [20], [13], [21], [25], [50], [11], [37], [22], [38], 22 [61], [51], [62], [52], [26], [27], [23], [24], [53], [54], [39] 2 ELSIVER [65], [15], [66], [59] 4…”

Section: Quality Assessmentmentioning

confidence: 99%

A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoption

Muzaffar¹,

Tahir²,

Anwar³

et al. 2020

Preprint

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E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters, wars, and pandemic like COVID 2019. The reliable, fair, and seamless execution of online exams in E-learning is highly significant. Particularly, online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place. This poses several issues like integrity and security during online exams. To address such issues, researchers frequently proposed different techniques and tools. However, a study summarizing and analyzing latest developments, particularly in the area of online examination, is hard to find in the literature. In this article, a Systematic Literature Review (SLR) of online examination is performed to select and analyze 53 studies published during the last five years (i.e. Jan 2016 to July 2020). Subsequently, five leading online exams features targeted in the selected studies are identified. Moreover, underlying development approaches for the implementation of online exams solutions are explored. Furthermore, 16 important techniques / algorithms and 11 datasets are presented. In addition to this, 21 online exams tools proposed in the selected studies are identified. Additionally, 25 leading existing tools used in the selected studies are also presented. Finally, the par-

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“…The fact is that the students adopt different cheating approaches during the examination. There are psychological and social reasons why students opt to cheat in exams, such as parent's pressure, feeling of incompetency, want better grades, time constraints, fear of failure, and take a risk with less fear of being caught [1]. The research of Dr. Donald McCabe's and the International Centre for Academic Integrity survey and analysis states that about 68% of undergrads admit to using unfair means or cheating during exams [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Invigilation Using Computer Vision

Malhotra¹,

Chhabra²

2021

Atlantis Highlights in Computer Sciences

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Educational institutions determine students' strengths and weaknesses through exams. Students find numerous ways to cheat in physical exams like exchanging their sheets, using hidden notes, getting good grades, fulfilling their parents' expectations, and whatnot. Due to the physical limitations of human supervisors, typical invigilation methods cannot conduct successful exams while maintaining their integrity. An automated method based on computer vision to detect anomalous activities during exams is proposed in this study. This study centers around invigilating students' suspicious behaviour during physical exams through closed-circuit television (CCTV) cameras. The proposed method uses You Only Look Once (YOLOv3) with residual networks as the backbone architecture to inspect cheating in exams. The obtained results show the credibility and efficiency of the proposed method. The experimental results are promising and demonstrate the invigilation of the students in the examination. In this work, achieve 88.03% accuracy for the detection of cheating in the classroom environment

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A New Model of Automatic and Continuous Online Exam Monitoring

Cited by 43 publications

References 18 publications

Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoption

Automatic Invigilation Using Computer Vision

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