Identifying Cheating Users in Online Courses

Sangalli, Vicenzo Abichequer; Martínez-Muñoz, Gonzalo; Cañabate, Estrella Pulido

doi:10.1109/educon45650.2020.9125252

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Their results indicated high detection rates (sensitivity and specificity measures of 0.966 and 0.996, respectively). Equally, Sangalli et al (2020) achieve a 95% generalization accuracy in classifying instances of academic fraud using a Support Vector Machine algorithm.…”

Section: Harnessing Ai Technology To Detect and Deter Academic Fraudmentioning

confidence: 99%

The impact of ChatGPT on higher education

Dempere,

Modugu,

Hesham

et al. 2023

Front. Educ.

129

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IntroductionThis study explores the effects of Artificial Intelligence (AI) chatbots, with a particular focus on OpenAI’s ChatGPT, on Higher Education Institutions (HEIs). With the rapid advancement of AI, understanding its implications in the educational sector becomes paramount.MethodsUtilizing databases like PubMed, IEEE Xplore, and Google Scholar, we systematically searched for literature on AI chatbots’ impact on HEIs. Our criteria prioritized peer-reviewed articles, prominent media outlets, and English publications, excluding tangential AI chatbot mentions. After selection, data extraction focused on authors, study design, and primary findings. The analysis combined descriptive and thematic approaches, emphasizing patterns and applications of AI chatbots in HEIs.ResultsThe literature review revealed diverse perspectives on ChatGPT’s potential in education. Notable benefits include research support, automated grading, and enhanced human-computer interaction. However, concerns such as online testing security, plagiarism, and broader societal and economic impacts like job displacement, the digital literacy gap, and AI-induced anxiety were identified. The study also underscored the transformative architecture of ChatGPT and its versatile applications in the educational sector. Furthermore, potential advantages like streamlined enrollment, improved student services, teaching enhancements, research aid, and increased student retention were highlighted. Conversely, risks such as privacy breaches, misuse, bias, misinformation, decreased human interaction, and accessibility issues were identified.DiscussionWhile AI’s global expansion is undeniable, there is a pressing need for balanced regulation in its application within HEIs. Faculty members are encouraged to utilize AI tools like ChatGPT proactively and ethically to mitigate risks, especially academic fraud. Despite the study’s limitations, including an incomplete representation of AI’s overall effect on education and the absence of concrete integration guidelines, it is evident that AI technologies like ChatGPT present both significant benefits and risks. The study advocates for a thoughtful and responsible integration of such technologies within HEIs.

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Section: Harnessing Ai Technology To Detect and Deter Academic Fraudmentioning

confidence: 99%

The impact of ChatGPT on higher education

Dempere,

Modugu,

Hesham

et al. 2023

Front. Educ.

129

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“…Testing Students: ML algorithms are being used to assess students and provide constant feedback to teachers both in a classroom and blended learning setting. Several studies have explored ML based approaches to combat cheating and ensure the academic integrity of student assessments (Kamalov et al, 2021;Sangalli et al, 2020). In online education, it is particularly changeling for professors to supervise students during exams increasing the risk of academic misconduct.…”

Section: Education Quality Experience and Satisfactionmentioning

confidence: 99%

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

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In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.

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“…Along with this, a number of downsides in full-scale online training have been identified. Particularly, in massive open online courses (MOOCs), dropout rates reach 90% in a number of cases [11,12].…”

Section: Expanding the View Of Blended Learningmentioning

confidence: 99%

Active Blended Learning Engineering Students: A Case Study

2022

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An ongoing technological, economic, and societal change forces a new understanding of engineering and modifies the requirements for higher engineering education. Consequently, an educational approach based on the combination of blended learning and active learning promises great prospects for the enhancement of the knowledge and skills acquisition thanks to the flexibility it opens up. In this research, an attitude to study is first ever analysed from the standpoint of students enrolled in three various degree levels, namely, a Bachelor of Science program, a Master of Science program, and a new EuroTeQ program. The strengths and weaknesses of the blended and active learning combination are evaluated from didactic and methodological sides. It shows a fundamentally different approach to learning from students belonging to various degree programs. Most of the bachelor degree learners volunteered to participate in active learning, although a significant proportion of them was unable to succeed. In contrast, the learning of master students looks more circumspect and selective; they devote less time to study, but spend it more wisely, being more highly responsible for the outcomes than bachelors. The EuroTeQ participants did not push on many active learning activities, but demonstrated quite high motivation in quizzes, labs, and online consultations. As a result, the outcomes of the first academic year satisfied mainly the most strong of the EuroTeQ students.

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Identifying Cheating Users in Online Courses

Cited by 10 publications

References 22 publications

The impact of ChatGPT on higher education

The impact of ChatGPT on higher education

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Active Blended Learning Engineering Students: A Case Study

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