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Reporting Less Coincidental Similarity to Educate Students about Programming Plagiarism and Collusion
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Oscar Karnalim; Simon; William Chivers – Computer Science Education, 2024
Background and Context: To educate students about programming plagiarism and collusion, we introduced an approach that automatically reports how similar a submitted program is to others. However, as most students receive similar feedback, those who engage in plagiarism and collusion might feel inadequately warned. Objective: When students are…
Descriptors: Teaching Methods, Plagiarism, Computer Science Education, Programming
Countering the "Plagiarism Slot Machine": Protecting Creators and Businesses from AI Copyright Infringement
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Christine Ladwig; Dana Schwieger; Reshmi Mitra – Information Systems Education Journal, 2025
The rapid rise of AI use is creating some very serious legal and ethical issues such as bias, discrimination, inequity, privacy violations, and--as creators everywhere fear--theft of protected intellectual property. Because AI platforms "learn" by scraping training materials available online or what is provided to them through their…
Descriptors: Copyrights, Plagiarism, Intellectual Property, Computer Software
Computational Notebooks in Chemical Engineering Curricula
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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
Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
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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
Proceedings of the International Conference on Educational Data Mining (EDM) (13th, Online, July 10-13, 2020)
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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
Proceedings of the International Conference on Educational Data Mining (EDM) (11th, Raleigh, North Carolina, July 16-20, 2018)
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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