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Showing 1 to 15 of 25 results Save | Export
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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Kori, Külli; Pedaste, Margus; Must, Olev – ACM Transactions on Computing Education, 2018
Low retention rates in higher education Information Technology (IT) studies have led to an unmet demand for IT specialists. Therefore, universities need to apply interventions to increase retention rates and provide the labor market with more IT graduates. However, students with different characteristics may need different types of interventions.…
Descriptors: Information Technology, Computer Science Education, Intervention, School Holding Power
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Kitikanan, Patchanok – English Language Teaching, 2020
Perceptual assimilation is a well-known task; however, there is no study on the assimilation pattern of the English monophthongs by L2 Thai learners. The aims of this study are to explore the perceptual assimilation patterns of the British English monophthongs to Thai monophthongs by L2 Thai learners and to examine the effect of L2 experience on…
Descriptors: English (Second Language), Second Language Learning, Language Variation, Thai
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