Cutting the vicious circle: Addressing the inconsistency in teachers’ approaches to academic integrity breaches

Dysfunctional educational system has been identified as one of the causes of academic dishonesty in Eastern Europe. This case study combines quantitative self-reported data and qualitative data from students and teachers with hard data from the disciplinary committee, collected at one Czech university. We analyse cases and types of breaches, identify characteristics of students that incline them toward cheating and investigate some of the reasons why. Our research confirms that the inconsistent approach of teachers is a contributing factor to students’ propensity to violate academic integrity rules and identifies reasons for such behaviour. Teachers play a key role in prevention, it is their duty to report cases of suspected misconduct, but they need tools to improve the culture of academic integrity. The contribution of this paper is to provide an inspiration for policy makers how to tackle the inconsistency of teachers’ approaches to student misconduct

Identifying Machine-Paraphrased Plagiarism

Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99% (F1=99.68% for SpinBot and F1=71.64% for SpinnerChief cases), while human evaluators achieved F1=78.4% for SpinBot and F1=65.6% for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan. To facilitate future research, all data, code, and two web applications showcasing our contributions are openly available

Testing of Detection Tools for AI-Generated Text

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for artificial intelligence generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AIgenerated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AIgenerated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.Comment: 38 pages, 13 figures and 10 tables, with appendix. Submitted to the International Journal of Educational Technology in Higher Educatio

Testing of Support Tools for Plagiarism Detection

There is a general belief that software must be able to easily do things that humans find difficult. Since finding sources for plagiarism in a text is not an easy task, there is a wide-spread expectation that it must be simple for software to determine if a text is plagiarized or not. Software cannot determine plagiarism, but it can work as a support tool for identifying some text similarity that may constitute plagiarism. But how well do the various systems work? This paper reports on a collaborative test of 15 web-based text-matching systems that can be used when plagiarism is suspected. It was conducted by researchers from seven countries using test material in eight different languages, evaluating the effectiveness of the systems on single-source and multi-source documents. A usability examination was also performed. The sobering results show that although some systems can indeed help identify some plagiarized content, they clearly do not find all plagiarism and at times also identify non-plagiarized material as problematic

How to Avoid Plagiarism: Student Handbook

In this handbook you’ll learn: – how to formulate your own ideas – how to correctly reference different sources – what exactly constitutes plagiarism – how to avoid various forms of plagiarism – examples of (in)famous cases of plagiarism – three tips against plagiarism – and finally, some advice for avoiding time pressure

How to Avoid Plagiarism: Student Handbook

In this handbook you’ll learn: – how to formulate your own ideas – how to correctly reference different sources – what exactly constitutes plagiarism – how to avoid various forms of plagiarism – examples of (in)famous cases of plagiarism – three tips against plagiarism – and finally, some advice for avoiding time pressure

How to Avoid Plagiarism: Student Handbook

In this handbook you’ll learn: – how to formulate your own ideas – how to correctly reference different sources – what exactly constitutes plagiarism – how to avoid various forms of plagiarism – examples of (in)famous cases of plagiarism – three tips against plagiarism – and finally, some advice for avoiding time pressure

Jak předcházet psaní prací na zakázku

PublishedPsaní prací na zakázku (contract cheating) je přestupek, kdy osoba využije nepřiznanou nebo neoprávněnou třetí stranu, aby jí pomohla vypracovat práci za účelem získání zápočtu, kreditů, akademického postupu a podobné výhody. Širší veřejnosti je tato problematika známa zejména v souvislosti s nabídkami komerčních firem, které zpracování prací inzerují na internetu. Kniha ukazuje, jak psaní prací na zakázku předcházet, následně jsou popsány významné kauzy, legislativa a výzkumy v zahraničí i v České republice

How to Prevent Plagiarism in Student Work

This handbook provide clear yet sufficiently comprehensive guidelines for situations that may arise in connection with plagiarism in the day-to-day academic routine. The handbook offers the opportunity to consider not only the aspects of originality in student work and how to explain the importance of source referencing to students and forms of plagiarism, but also how to recognise plagiarism and what software tools can be used for this purpose. Further, the handbook describes applying penalties for plagiarism and summarises the fundamental antiplagiarism advice from a teacher’s perspective into several practical pieces of advice