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Mike Perkins; Jasper Roe; Darius Postma; James McGaughran; Don Hickerson – Journal of Academic Ethics, 2024
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI's ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors…
Descriptors: Artificial Intelligence, Student Evaluation, Identification, Natural Language Processing
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Brian P. Shaw – Music Educators Journal, 2024
In the coming years, the proliferation of artificial intelligence (AI) will lead to changes and challenges to many traditional practices in school music and beyond, particularly related to student assessment and grading. At the same time, the AI revolution may also facilitate new and exciting directions for assessment and differentiation in music…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Music Education
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Daire Maria Ni Uanachain; Lila Ibrahim Aouad – Thresholds in Education, 2025
This chapter investigates the dual role of Generative AI (GenAI) in providing support and feedback to students and in reshaping formative and summative educational assessments, addressing both the burgeoning opportunities for enhancing teaching methodologies and the associated ethical challenges. There is a focus on the necessity for balanced…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Teaching Methods
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Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
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Jeske, Heimo J.; Lall, Manoj; Kogeda, Okuthe P. – Journal of Information Technology Education: Innovations in Practice, 2018
Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of…
Descriptors: Plagiarism, Identification, Computer Software, Computer Assisted Testing
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Haus, Goffredo; Pasquinelli, Yuri Benvenuto; Scaccia, Daniela; Scarabottolo, Nello – International Association for Development of the Information Society, 2020
The paper deals with the problem of carrying on online written exams in the University of Milan, suddenly closed due to the Covid-19 emergency. Main goal of the paper is to present and compare the different scenarios envisioned, depending on the number of students to be monitored in parallel to avoid cheating. For limited numbers, direct…
Descriptors: COVID-19, Pandemics, Computer Assisted Testing, Cheating
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Mellar, Harvey; Peytcheva-Forsyth, Roumiana; Kocdar, Serpil; Karadeniz, Abdulkadir; Yovkova, Blagovesna – International Journal for Educational Integrity, 2018
Student authentication and authorship checking systems are intended to help teachers address cheating and plagiarism. This study set out to investigate higher education teachers' perceptions of the prevalence and types of cheating in their courses with a focus on the possible changes that might come about as a result of an increased use of…
Descriptors: Cheating, Incidence, Integrity, Computer Software
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Seifried, Eva; Lenhard, Wolfgang; Spinath, Birgit – Psychology Learning and Teaching, 2015
Essays that are assigned as homework in large classes are prone to cheating via unauthorized collaboration. In this study, we compared the ability of a software tool based on Latent Semantic Analysis (LSA) and student teaching assistants to detect plagiarism in a large group of students. To do so, we took two approaches: the first approach was…
Descriptors: Plagiarism, Cheating, Essays, Homework
Betts, Lucy R.; Bostock, Stephen J.; Elder, Tracey J.; Trueman, Mark – Psychology Teaching Review, 2012
There is growing concern among many regarding plagiarism within student writing. This has promoted investigation into both the factors that predict plagiarism and potential methods of reducing plagiarism. Consequently, we developed and evaluated an intervention to enhance good practice within academic writing through the use of the plagiarism…
Descriptors: Cheating, Intervention, Teaching Methods, Writing (Composition)
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Pribela, Ivan; Ivanovic, Mirjana; Budimac, Zoran – British Journal of Educational Technology, 2009
This paper discusses Svetovid, cross-platform software that helps instructors to assess the amount of effort put into practical exercises and exams in courses related to computer programming. The software was developed as an attempt at solving problems associated with practical exercises and exams. This paper discusses the design and use of…
Descriptors: Computer Software, Prevention, Classroom Techniques, Student Behavior
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Lallie, Harjinder Singh; Lawson, Phillip; Day, David J. – Journal of Information Technology Education: Innovations in Practice, 2011
Identifying academic misdemeanours and actual applied effort in student assessments involving practical work can be problematic. For instance, it can be difficult to assess the actual effort that a student applied, the sequence and method applied, and whether there was any form of collusion or collaboration. In this paper we propose a system of…
Descriptors: Foreign Countries, Evidence, Computer Software, Academic Achievement
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Marks, Anthony M.; Cronje, Johannes C. – Educational Technology & Society, 2008
Computer-based assessments are becoming more commonplace, perhaps as a necessity for faculty to cope with large class sizes. These tests often occur in large computer testing venues in which test security may be compromised. In an attempt to limit the likelihood of cheating in such venues, randomised presentation of items is automatically…
Descriptors: Educational Assessment, Educational Testing, Research Needs, Test Items
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Peng, Zhuoming – Journal of Educators Online, 2007
The primary benefit of providing out-of-class online quizzes in a face-to-face class is to gain more in-class time. A study designed to investigate this issue was conducted during the Spring 2006 and Spring 2007 semesters. Thirty-one and 34 Corporate Finance undergraduate students from each semester, and 33 and 36 Investments undergraduate…
Descriptors: Undergraduate Students, Cheating, Tests, Business Administration Education
Cizek, Gregory J. – 2003
This book provides a resource for everything educators need to know about classroom cheating. Six chapters include: (1) "What Do We Know About Cheating in the Classroom" (who is cheating and why); (2) "Why is Cheating a Problem?" (e.g., the ubiquity and the consequences of cheating); (3) "How Does Cheating Occur?"…
Descriptors: Cheating, Classroom Environment, Codes of Ethics, Computer Software
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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