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Cheers, Hayden; Lin, Yuqing – Computer Science Education, 2023
Background and Context: Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism…
Descriptors: Plagiarism, Programming, Computer Science Education, Identification
Karnalim, Oscar; Simon; Chivers, William – IEEE Transactions on Learning Technologies, 2023
We have recently developed an automated approach to reduce students' rationalization of programming plagiarism and collusion by informing them about the matter and reporting uncommon similarities to them for each of their submissions. Although the approach has benefits, it does not greatly engage students, which might limit those benefits. To…
Descriptors: Gamification, Programming, Plagiarism, Cooperative Learning
Cheers, Hayden; Lin, Yuqing; Yan, Weigen – Informatics in Education, 2023
Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work…
Descriptors: Plagiarism, Assignments, Computer Software, Computer Science Education
Oscar Karnalim – Informatics in Education, 2024
Programming students need to be informed about plagiarism and collusion. Hence, we developed an assessment submission system to remind students about the matter. Each submission will be compared to others and any similarities that do not seem a result of coincidence will be reported along with their possible reasons. The system also employs…
Descriptors: Programming, Integrity, Academic Achievement, Plagiarism
Verrett, Jonathan; Boukouvala, Fani; Dowling, Alexander; Ulissi, Zachary; Zavala, Victor – Chemical Engineering Education, 2020
Computational notebooks are an increasingly common tool used to support student learning in a variety of contexts where computer programming can be applied. These notebooks provide an easily distributable method of displaying text and images, as well as sections of computer code that can be manipulated and run in real-time. This format allows…
Descriptors: Computer Science Education, Programming, Programming Languages, College Students
Novak, Matija; Joy, Mike; Kermek, Dragutin – ACM Transactions on Computing Education, 2019
Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers.…
Descriptors: Plagiarism, Computer Software, Computer Software Evaluation, College Students
Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation
Karnalim, Oscar; Budi, Setia; Toba, Hapnes; Joy, Mike – Informatics in Education, 2019
Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these…
Descriptors: Plagiarism, Computer Science Education, Comparative Analysis, Problem Solving
Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Hattingh, Frederik; Buitendag, Albertus A. K.; van der Walt, Jacobus S. – Journal of Information Technology Education: Innovations in Practice, 2013
The transfer and teaching of programming and programming related skills has become, increasingly difficult on an undergraduate level over the past years. This is partially due to the number of programming languages available as well as access to readily available source code over the Web. Source code plagiarism is common practice amongst many…
Descriptors: Plagiarism, Identification, Programming, Computer Science Education
Ahmadzadeh, Marzieh; Mahmoudabadi, Elham; Khodadadi, Farzad – Journal of Information Technology Education, 2011
Anecdotal evidence shows that in computer programming courses plagiarism is a widespread problem. With the growing number of students in such courses, manual plagiarism detection is impractical. This requires instructors to use one of the many available plagiarism detection tools. Prior to choosing one of such tools, a metric that assures the…
Descriptors: Foreign Countries, Plagiarism, Programming Languages, Computer Software
Mozgovoy, Maxim; Kakkonen, Tuomo; Cosma, Georgina – Journal of Educational Computing Research, 2010
The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to…
Descriptors: Plagiarism, Alphabets, Internet, Ethics
Knight, Allan; Almeroth, Kevin – Journal of Interactive Learning Research, 2011
As part of the research carried out at the University of California, Santa Barbara's Center for Information Technology and Society (CITS), the Paper Authentication and Integrity Research (PAIR) project was launched. We began by investigating how one recent technology affected student learning outcomes. One aspect of this research was to study the…
Descriptors: Plagiarism, Student Attitudes, Form Classes (Languages), Researchers
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Lancaster, Thomas; Culwin, Fintan – Computer Science Education, 2004
Automated techniques for finding plagiarism in student source code submissions have been in use for over 20 years and there are many available engines and services. This paper reviews the literature on the major modern detection engines, providing a comparison of them based upon the metrics and techniques they deploy. Generally the most common and…
Descriptors: Foreign Countries, Plagiarism, College Students, Student Evaluation