Dynamic Thresholding Mechanisms for IR-Based Filtering in Efficient Source Code Plagiarism Detection

To solve time inefficiency issue, only potential pairs are compared in string-matching-based source code plagiarism detection; wherein potentiality is defined through a fast-yet-order-insensitive similarity measurement (adapted from Information Retrieval) and only pairs which similarity degrees are higher or equal to a particular threshold is selected. Defining such threshold is not a trivial task considering the threshold should lead to high efficiency improvement and low effectiveness reduction (if it is unavoidable). This paper proposes two thresholding mechanisms---namely range-based and pair-count-based mechanism---that dynamically tune the threshold based on the distribution of resulted similarity degrees. According to our evaluation, both mechanisms are more practical to be used than manual threshold assignment since they are more proportional to efficiency improvement and effectiveness reduction.Comment: The 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS

Collaboration Versus Cheating

We outline how we detected programming plagiarism in an introductory online course for a master's of science in computer science program, how we achieved a statistically significant reduction in programming plagiarism by combining a clear explanation of university and class policy on academic honesty reinforced with a short but formal assessment, and how we evaluated plagiarism rates before SIGand after implementing our policy and assessment.Comment: 7 pages, 1 figure, 5 tables, SIGCSE 201

STRATEGI BIMBINGAN KELOMPOK MELALUI TRAINING GROUP DALAM PENGEMBANGAN INTEGRITAS AKADEMIK SISWA

Siswa dengan integritas akademik rendah cenderung untuk melakukan pelanggaran akademik dan menganggap pelanggaran akademik menjadi hal biasa. Penelitian ini bertujuan untuk menguji keefektifan strategi bimbingan kelompok melalui training group dalam pengembangan integritas akademik siswa. Metode yang digunakan adalah metode eksperimen dengan desain non equivalent pretest-posttest control group design. Partisipan penelitian berjumlah 24 orang (12 kelompok eksperimen dan 12 kelompok kontrol) yang dipilih secara purposive pada siswa yang memiliki integritas akademik rendah. Instrumen penelitian berupa angket integritas akademik siswa. Data dianalisis menggunakan uji perbedaan (U-Mann-Withney) dengan membandingkan rerata skor pada kelompok eksperimen dan kelompok kontrol. Hasil penelitian menunjukkan strategi bimbingan kelompok melalui training group cukup efektif dalam pengembangan integritas akademik siswa. Kata Kunci : Integritas Akademik, bimbingan kelompok, training group Students who had low academic integrity tend to commit academic violations and consider academic violations to be commonplace. This study aims to test the effectiveness of group guidance strategies through group training in developing students' academic integrity. The method used is an experimental method with a non equivalent pretest-posttest control group design. The number of participants in this study 24 students (12 experimental groups and 12 control groups) selected by purposive from students who had low academic integrity. The instrument used in this study is the academic integrity scale for students. Data were analyzed using the difference test (U-Mann-Whitney) by comparing the mean scores in experimental group and control group. Test results showed the group guidance strategy through group training was quite effective in developing students' academic integrity. Key Word : Academic Integrity, Group Guidance, Training Grou

Plagiarism and AI Assistance Misuse in Web Programming: Unfair Benefits and Characteristics

In programming education, plagiarism and misuse of artificial intelligence (AI) assistance are emerging issues. However, not many relevant studies are focused on web programming. We plan to develop automated tools to help instructors identify both misconducts. To fully understand the issues, we conducted a controlled experiment to observe the unfair benefits and the characteristics. We compared student performance in completing web programming tasks independently, with a submission to plagiarize, and with the help of AI assistance (ChatGPT). Our study shows that students who are involved in such misconducts get comparable test marks with less completion time. Plagiarized submissions are similar to the independent ones except in trivial aspects such as color and identifier names. AI-assisted submissions are more complex, making them less readable. Students believe AI assistance could be useful given proper acknowledgment of the use, although they are not convinced with readability and correctness of the solutions.Comment: Accepted at IEEE TALE 202

Effects of Plagiarism in Introductory Programming Courses on the Learning Outcomes

[EN] We compare two introductory programming courses and the accompanying programming assignments with respect to the learning outcomes and the relation to plagiarism. While in the first course the solutions from the students of their programming assignments are checked directly with a plagiarism detection system to prevent students from plagiarizing, plagiarism is not tracked in the second course. Running a post check against plagiarism after the course reveals a significant higher plagiarism rate with several exact copies. As the number of students handing in copies from fellow students increases, the failure rate in the final examination also rises. Analyzing the data does not only reveal a correlation between plagiarizing and inferior examination results, but also shows, that students confronted with a plagiarism detection system have better skills in fundamental coding concepts. We suppose this might be a result of the fact, that the implementation of a plagiarism detection system does not deter so many students from plagiarizing, but students are strongly motivated to run more modifications on their plagiarisms in order not to be caught.Pawelczak, D. (2019). Effects of Plagiarism in Introductory Programming Courses on the Learning Outcomes. En HEAD'19. 5th International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 623-631. https://doi.org/10.4995/HEAD19.2019.9297OCS62363

Annual Report 2017-2018

LETTER FROM THE DEAN I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2017-18 annual report, highlighting the many achievements across our community. It was a big year. We began offering five new programs (two bachelor’s, two master’s, and one PhD) across our three schools, in addition to several new certificate programs through our Institute for Professional Development. We built new, cutting-edge spaces to support these and other programs— most notably a 4,500 square-foot makerspace, a robotics and medical engineering lab, an augmented and virtual reality lab, and plans for a cyber-physical systems project lab. Our faculty continued to pursue their research and creative agendas, offering collaborative opportunities with students and partners. CDM students and alumni were celebrated for their many achievements— everything from leading the winning teams at the U.S. Cyber Challenge and Campus 1871 to showcasing their games at juried festivals and winning national screenwriting competitions. We encouraged greater research and teaching collaboration, both between our own schools and with units outside CDM. Design and Computing faculty are working together on an NSA grant for smart home devices that considers both software and interface/design, as well as a new grant-funded game lab. One Project Bluelight film team collaborated with The Theatre School and the School of Music while CDM and College of Science and Health faculty joined forces to research the links between traumatic brain injury, domestic violence, and deep games. It has been exciting and inspiring to witness the accomplishments of our innovative and dedicated community. We are proud to provide the space and resources for them to do their exceptional work. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1001/thumbnail.jp

The Robots are Here: Navigating the Generative AI Revolution in Computing Education

Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret source code and natural language instructions. These emergent capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of LLMs in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents who have already adapted their curricula and assessments. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of LLMs on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving. Our aim is that this report will serve as a focal point for both researchers and practitioners who are exploring, adapting, using, and evaluating LLMs and LLM-based tools in computing classrooms

Informing Students about Academic Integrity in Programming.

In recent years academic integrity has come to be seen as a major concern across the full educational spectrum. The case has been made that in certain ways academic integrity is not the same in computing education as in education more generally, and that as a consequence it is the responsibility of computing educators to explicitly advise their students of the academic integrity requirements of their assessments. As part of a larger project, computing academics around the world were asked a number of questions regarding how they advise their students about academic integrity in programming assessments. Almost all respondents indicated that their students were required to abide by an academic integrity policy, but only about half of them felt that the policy was appropriate for programming assessments. We analyse respondents\u27 descriptions of how they advise students about academic integrity in programming assessments, grouping them into a number of themes, and give excerpts from the guidelines that some respondents provide for their students. A clear finding from the survey is that while most educators agree that externally sourced code or assistance should be acknowledged, fewer than 20% are aware of any standard form for that acknowledgement. We therefore conclude by proposing, for discussion, a standard form for acknowledging externally sourced code or assistance