Implementation of a remote system for the supervision of online exams through the use of cameras with artificial intelligence

Padilla, Alfredo Barrientos; Cuadros, Miguel; Alba, Jean; Cruz, Angel Santa

doi:10.1109/eircon52903.2021.9613352

Cited by 4 publications

(2 citation statements)

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“…In addition, Barrientos et al [24] aimed to leverage Amazon Web Services (AWS) to detect dishonest behavior among examiners, such as using smartphones for searching answers or detecting plagiarism. They employed face detection and TensorFlow as the method.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Cheating Detection for Online Examination Using Clustering Based Approach

Ong,

Connie,

Goh

2023

JOIV : Int. J. Inform. Visualization

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Online exams have become increasingly popular due to their convenience in eliminating the need for physical exams and allowing students to take exams from remote locations. However, one of the drawbacks of online exams is that they make cheating easier, and it can be difficult for online proctoring to detect subtle movements by the students. This could lead to doubts about students' exam results' value and overall credibility. To address this pressing issue, we present a cheating detection method using a CCTV camera to monitor students' faces, eyes, and devices to determine whether they cheat during exams. If suspicious behavior indicative of cheating is detected, a warning is raised to alert the students. A custom dataset was developed to train the model. The dataset consisted of recordings of pre-determined cheating behavior by 50 participants. These videos captured various poses and behaviors encoded and analyzed using a clustering approach. The encoded clustering method continuously tracks the students' faces, eyes, and body gestures throughout an exam. Experimental results show that the proposed approach effectively detects cheating behavior with a favorable accuracy of 83%. The proposed method offers a promising solution to the growing concern about cheating in online exams. This approach can significantly enhance the integrity and reliability of online assessment processes, fostering trust among educational institutions and stakeholders.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Cheating Detection for Online Examination Using Clustering Based Approach

Ong,

Connie,

Goh

2023

JOIV : Int. J. Inform. Visualization

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“…The integration of sophisticated technology, especially artificial intelligence (AI), into educational settings has further complicated this discourse (Barrientos, Cuadros, Alba & Cruz, 2021;Benvenuti et al, 2023). In the field of examinations and assignments, the ChatGPT-4, a sophisticated language model developed by OpenAI, distinguishes itself using its capacity to generate a narrative that exhibits human-like composition.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based large language models and integrity of exams and assignments in higher education: the case of tourism courses

Ülkü

2023

TMS

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There is an increasing concern regarding the potential misuse of ChatGPT-4 in compromising the integrity of examinations and assignments. This study aims to examine the capabilities of ChatGPT-4 in critical thinking abilities, whether it poses a threat to examinations and assignments in higher education, and create a discussion agenda on this issue. ChatGPT-4 was asked to generate, answer, and criticize questions in tourism marketing, tourism management, tourism economics, tourist guidance, and gastronomy. The answers were evaluated according to universal critical thinking standards. The findings obtained from this study showed that ChatGPT-4 had commendable competence in several critical thinking standards and could produce human-like texts. However, there are certain domains that might be improved to comply more effectively with the expectations and norms of academia. Educators were recommended to use comprehensive approaches that combine technological and educational techniques to address the issue of cheating enabled by tools, such as ChatGPT-4, during assessment and exam processes.

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