A Multi-index Examination Cheating Detection Method Based on Neural Network

Li, Zhizhuang; Zhu, Zhengzhou; Yang, Teng

doi:10.1109/ictai.2019.00086

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…A number of researchers (Wesolowsky, 2000; Lin and Levitt, 2020) have developed methods of identifying cheating, and have asserted that “interventions that make cheating more difficult dramatically reduce cheating” (Lin and Levitt, 2020, p. 899). Some have used algorithms (Lin and Levitt, 2020) while others have used neural networks (Li et al , 2019) because detecting manually is no longer possible (Nishchal et al , 2020). Nishchal et al , have developed a model by incorporating both posture and emotion analysis through the use of closed circuit television cameras a model that would not be accepted by many universities nowadays due to the EU Council Directive (2016) on general data protection regulation (GDPR).…”

Section: Cheating Methods and Countermeasuresmentioning

confidence: 99%

“…The Organisation for Economic Co-operation and Development (OECD, 2011) specified that there are eight broad areas of academic dishonesty which include: obtaining unauthorised aid or information; giving unauthorised aid or information; committing plagiarism from written, electronic or internet sources; misrepresenting facts or data; offering bribes; using the library resources unethically; using computer resources unethically; and knowingly assisting in any of the above practices. Li et al (2019) have argued that cheating in examinations destroys the "fair principle". Peculean and Peculea (2020) cite the International Center's Academic Integrity core values which are as follows: "honesty, trust, fairness, respect, responsibility and courage" and set the baseline of what is acceptable behaviour (Ranger et al, 2020).…”

Section: Perspectives On Cheating In Examinationsmentioning

confidence: 99%

“…Li et al (2019) have argued that cheating in examinations destroys the “fair principle”. Peculean and Peculea (2020) cite the International Center’s Academic Integrity core values which are as follows: “honesty, trust, fairness, respect, responsibility and courage” and set the baseline of what is acceptable behaviour (Ranger et al , 2020).…”

Section: Perspectives On Cheating In Examinationsmentioning

confidence: 99%

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Lessons learned during Covid-19 concerning cheating in e-examinations by university students

Krambia‐Kapardis

Spanoudis

2021

JFC

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Purpose The researchers aim to investigate how students can be deterred from cheating, whether legal or ethical policies and procedures are effective and whether there are gender differences. Design/methodology/approach Using data on students undertaking midterm and final e-examinations, as well as a control group of students who were caught cheating in an online mid-semester examination, the authors attempt to answer the research questions. Findings No differences were found in cheating in terms of students’ gender or whether they were repeating a course or not. However, the study revealed that if there are more internal controls imposed and if before the examination students are made to reinforce their academic integrity, e-examination cheating is reduced. Originality/value No other published study was carried out with students who were involved in cheating.

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Section: Cheating Methods and Countermeasuresmentioning

confidence: 99%

Section: Perspectives On Cheating In Examinationsmentioning

confidence: 99%

Section: Perspectives On Cheating In Examinationsmentioning

confidence: 99%

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Lessons learned during Covid-19 concerning cheating in e-examinations by university students

Krambia‐Kapardis

Spanoudis

2021

JFC

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“…There are very few articles in the literature that address detecting suspicious activity during examination through video datasets to facilitate invigilators efficient conduction of exams. The authors in [22] provide a framework to monitor student activities during examination by detecting face region using Haar features, detecting hand contact and hand signalling as cheating activities, and raising an alert. Works on the detection of suspicious activity during academic offline examination.…”

Section: Related Workmentioning

confidence: 99%

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Ramzan¹,

Abid²,

Awan³

2022

Computers, Materials &Amp; Continua

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In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

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“…Plagiarism, another form of cheating or showing some other person's work as your own, is found by Li [6]. And designed a model using the RAE algorithm with the LSTM network and scored 79.4% accuracy in his work.…”

Section: Related Workmentioning

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 Multi-index Examination Cheating Detection Method Based on Neural Network

Cited by 11 publications

References 8 publications

Lessons learned during Covid-19 concerning cheating in e-examinations by university students

Lessons learned during Covid-19 concerning cheating in e-examinations by university students

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Automatic Invigilation Using Computer Vision

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