“I didn’t copy his code”: Code Plagiarism Detection with Visual Proof

John, Samuel; Boateng, George

doi:10.1007/978-3-030-78270-2_37

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Each section of the course ends with multiple-choice quizzes and a coding assignment. The assignments are graded by our automated grading software, Auto-Grad [11] which also checks for plagiarism [12] and provides detailed individualized explanations for wrong answers with an option for learners to submit a complaint in cases where they disagree with the provided answers and feedback. Our course-specific, AI-powered, human-in-the-loop forum is designed to allow learners to ask questions (even anonymously) and get quick and accurate answers from their peers, facilitators, and our AI teaching assistant, Kwame [13].…”

Section: Suacodementioning

confidence: 99%

“…We developed a tool that performs code plagiarism detection in students' assignment submissions in SuaCode courses and also shows visual evidence of the detected plagiarism [12]. We built this tool to address the issues of code plagiarism in the SuaCode where we previously identified that 27% of the 431 students had cases of plagiarism via manual inspection.…”

Section: Code Plagiarism Detectectormentioning

confidence: 99%

“…Also, the system provides proof of plagiarism via a GUI tool that displays side-by-side pairs of code files while highlighting sections with overlapping code. This work is the first that builds a complete end-to-end system that uses (1) machine learning algorithms to detect plagiarized source code containing English and French texts while taking the code example provided by the instructors into consideration and (2) provides visual evidence of the plagiarism (Figure 5) [12].…”

Section: Code Plagiarism Detectectormentioning

confidence: 99%

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Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Boateng

2021

Lecture Notes in Computer Science

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Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of our students -learners across 38 African countries -in this work, we developed an AI Teaching Assistant (TA) -Kwame -that provides answers to students' coding questions from our SuaCode courses in English and French. Kwame is a Sentence-BERT(SBERT)-based question-answering (QA) system that we trained and evaluated using question-answer pairs created from our course's quizzes and students' questions in past cohorts. It finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that SBERT performed the worst for the duration of 6 secs per question but the best for accuracy and fine-tuning on our course data improved the result.

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Section: Suacodementioning

confidence: 99%

Section: Code Plagiarism Detectectormentioning

confidence: 99%

Section: Code Plagiarism Detectectormentioning

confidence: 99%

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Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Boateng

2021

Lecture Notes in Computer Science

Self Cite

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Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism

Manyrath,

Kirubel,

Cruz

2023

LJSS

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In an era marked by the increasing digitization of society, the issue of source code plagiarism has emerged as a persistent concern. This research paper delves into the problem of source code plagiarism within educational settings, exploring its implications, potential remedies, and the associated hurdles in implementing these solutions. Source code plagiarism involves the unauthorized copying of code without proper attribution, and it has been on the rise in educational institutions due to various contributing factors. This paper sheds light on the educational system's pressures, time constraints, lofty expectations, and the allure of quick completion that make source code plagiarism appealing to students. Furthermore, it highlights the lack of understanding among students regarding academic integrity and citation methods, exacerbating the problem. Source code plagiarism not only hampers students' intellectual development and problem-solving skills but also undermines the fairness of assessments, posing grading challenges for educators. Nevertheless, there are several potential solutions. While proactive methods focus on prevention through education and policy, reactive methods employ AI-driven plagiarism detectors for detection. However, these solutions are not without their challenges, such as the issue of false positives in plagiarism detection and the potential adversarial response from students. In conclusion, source code plagiarism is a growing problem in modern society that can not be avoided any longer. Potential solutions to source code plagiarism should be taken into account while considering their withdrawals. Computer science and programming courses should foster a sense of integrity to avoid source code plagiarism and develop new generations of coders for the future.

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“I didn’t copy his code”: Code Plagiarism Detection with Visual Proof

Cited by 2 publications

References 4 publications

Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism

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