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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
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
Matthew Quesnel; Brenda M. Stoesz – Australasian Journal of Educational Technology, 2025
Contract cheating is a significant concern in higher education, requiring a multifaceted teaching and learning approach to address it. Quizzing students about their writing to promote engagement, confirm authorship and detect cheating has not yet been investigated systematically. Therefore, in this study, our objective was to explore the validity…
Descriptors: Cheating, Authors, Computer Uses in Education, Validity
Khanittha Sittitiamjan; Pongpisit Wuttidittachotti – Educational Process: International Journal, 2025
Background/purpose: This study investigates how knowledge, attitudes, and practices (KAP) influence cybersecurity awareness (CSA) among students in Thai educational institutions. The research addresses regional disparities in cybersecurity readiness by incorporating a culturally responsive adaptation of the KAP model. Materials/methods: A…
Descriptors: Computer Security, Computer Science Education, Foreign Countries, College Students
Van Petegem, Charlotte; Deconinck, Louise; Mourisse, Dieter; Maertens, Rien; Strijbol, Niko; Dhoedt, Bart; De Wever, Bram; Dawyndt, Peter; Mesuere, Bart – Journal of Educational Computing Research, 2023
We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students (N = 2 080) and was found to be highly accurate and robust against variation in course…
Descriptors: Pass Fail Grading, At Risk Students, Introductory Courses, Programming
Livinus Obiora Nweke; Uchenna Franklin Okebanama; Gibson Uwaezuoke Mba – Discover Education, 2025
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and cybersecurity presents new opportunities for innovation and entrepreneurship, yet traditional educational approaches often lack the interdisciplinary and applied focus required to develop these competencies. This study evaluates the impact of an experiential learning…
Descriptors: Entrepreneurship, Artificial Intelligence, Internet, Information Security
Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
Varga, Erika B.; Sátán, Ádám – Hungarian Educational Research Journal, 2021
The purpose of this paper is to investigate the pre-enrollment attributes of first-year students at Computer Science BSc programs of the University of Miskolc, Hungary in order to find those that mostly contribute to failure on the Programming Basics first-semester course and, consequently to dropout. Our aim is to detect at-risk students early,…
Descriptors: Identification, At Risk Students, Computer Science Education, Undergraduate Students
Students as Contested: Exploring Issues of Student Identity and Identification in Educational Spaces
Ashley Kenway; Phil Wilkinson; Kieron Dowden-Smith – International Journal for Students as Partners, 2019
This article explores issues of student identity and identification through a third-space theory lens. In addition, it positions this use of third-space theory as contributory to Students-as-Partners (SaP) approaches to teaching and learning. Naturally, this research was constructed as a SaP project, and research was undertaken as a collaboration…
Descriptors: Self Concept, Undergraduate Students, Student Attitudes, Resistance (Psychology)
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
Alvarez, Niurys Lázaro; Callejas, Zoraida; Griol, David – Journal of Technology and Science Education, 2020
We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not…
Descriptors: Foreign Countries, College Students, Computer Science Education, Engineering Education
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
Ruiz, Samara; Urretavizcaya, Maite; Rodríguez, Clemente; Fernández-Castro, Isabel – Interactive Learning Environments, 2020
A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we…
Descriptors: College Students, Computer Science Education, Emotional Response, Student Reaction
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
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