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Showing 1 to 15 of 20 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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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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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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Jia, Jiyou; He, Yunfan – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic…
Descriptors: Artificial Intelligence, Supervision, Computer Assisted Testing, Electronic Learning
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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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Anna Y. Q. Huang; Jei Wei Chang; Albert C. M. Yang; Hiroaki Ogata; Shun Ting Li; Ruo Xuan Yen; Stephen J. H. Yang – Educational Technology & Society, 2023
To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from…
Descriptors: Academic Achievement, Tutoring, Artificial Intelligence, Individualized Instruction
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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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Jeske, Heimo J.; Lall, Manoj; Kogeda, Okuthe P. – Journal of Information Technology Education: Innovations in Practice, 2018
Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of…
Descriptors: Plagiarism, Identification, Computer Software, Computer Assisted Testing
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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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Smiley, Sarah L. – Journal of Geography, 2017
The reality show "The Amazing Race" serves as a useful text for the cultural geography classroom. As competitors travel the world, they complete challenging tasks designed to be both educational and entertaining. Audiences see actual images from destinations around the world and learn about the unique cultures of these places. They also…
Descriptors: Human Geography, Geography Instruction, Programming (Broadcast), Television
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Taherkhani, Ahmad; Malmi, Lauri – Journal of Educational Data Mining, 2013
In this paper, we present a method for recognizing algorithms from students programming submissions coded in Java. The method is based on the concept of "programming schemas" and "beacons". Schemas are high-level programming knowledge with detailed knowledge abstracted out, and beacons are statements that imply specific…
Descriptors: Programming, Mathematics, Computer Science Education, Methods
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Casey, Kevin – Journal of Learning Analytics, 2017
Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…
Descriptors: Keyboarding (Data Entry), Educational Research, Data Collection, Data Analysis
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Hattingh, Frederik; Buitendag, Albertus A. K.; van der Walt, Jacobus S. – Journal of Information Technology Education: Innovations in Practice, 2013
The transfer and teaching of programming and programming related skills has become, increasingly difficult on an undergraduate level over the past years. This is partially due to the number of programming languages available as well as access to readily available source code over the Web. Source code plagiarism is common practice amongst many…
Descriptors: Plagiarism, Identification, Programming, Computer Science Education
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