Testing of Support Tools for Plagiarism Detection

Foltýnek, Tomáš; Dlabolová, Dita; Anohina-Naumeca, Alla; Razı, Salim; Kravjar, Július; Kamzola, Laima; Guerrero-Dib, Jean; Çelik, Özgür; Weber-Wulff, Debora

doi:10.48550/arxiv.2002.04279

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Academic dishonesty represents a widespread challenge within higher education, impacting institutions globally (Foltýnek et al, 2020). This phenomenon encompasses a broad range of unethical practices, including but not limited to plagiarism, cheating on exams and assessment tests, falsification or fabrication of data, impersonation by advanced artificial intelligence writing tools, and the commercial exchange of academic activities or "contract cheating" (Cerdà-Navarro et al, 2023).…”

Section: Dishonest Conduct and Prevalencementioning

confidence: 99%

Academic integrity in pre-service teacher education: a review of the literature

Vallespir Adillón,

Espiñeira Bellón,

Muñoz Cantero

et al. 2024

PraxEduc

View full text Add to dashboard Cite

This research conducts a thorough review of academic integrity in teacher education, analysing the prevalence, reasons, and solutions for academic dishonesty. From an initial 693 articles in Web of Science and Scopus, 23 were selected based on strict criteria, revealing issues like plagiarism and cheating among aspiring teachers. The study points to academic pressure, skill shortages, and confusion about integrity principles as key factors driving dishonest behaviours. It stresses the importance of comprehensive strategies to combat this, including ethics education, the development of detection technologies, and cultivating a culture that prioritizes honesty. With the rise of AI, adapting integrity measures becomes crucial. Future research should focus on innovative methodologies, the effectiveness of ethics programs, and the impact of technology on maintaining academic standards, highlighting the educational community's responsibility to promote an honest environment, essential for the credibility of future educators.

show abstract

Section: Dishonest Conduct and Prevalencementioning

confidence: 99%

Academic integrity in pre-service teacher education: a review of the literature

Vallespir Adillón,

Espiñeira Bellón,

Muñoz Cantero

et al. 2024

PraxEduc

View full text Add to dashboard Cite

show abstract

“…Some of the existing paraphrase systems have performed quite well; however, there are certain challenges with paraphrase detection. For example, existing paraphrase systems deliver relatively good results for clean texts, but they do not perform well when applied to noisy texts [1][2][3]. Moreover, in recent years there was an expansion of deep neural network models' application to the NLP domain, and that opens up a complete new field for experimentation and improvement of the existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Corpus-Based Paraphrase Detection Experiments and Review

Vrbanec

Meštrović

2020

Information

View full text Add to dashboard Cite

Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection—where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.

show abstract

Testing of Support Tools for Plagiarism Detection

Cited by 2 publications

References 0 publications

Academic integrity in pre-service teacher education: a review of the literature

Academic integrity in pre-service teacher education: a review of the literature

Corpus-Based Paraphrase Detection Experiments and Review

Contact Info

Product

Resources

About