39 research outputs found
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