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Rick Somers; Sam Cunningham; Sarah Dart; Sheona Thomson; Caslon Chua; Edmund Pickering – IEEE Transactions on Learning Technologies, 2024
Academic misconduct stemming from file-sharing websites is an increasingly prevalent challenge in tertiary education, including information technology and engineering disciplines. Current plagiarism detection methods (e.g., text matching) are largely ineffective for combatting misconduct in programming and mathematics-based assessments. For these…
Descriptors: Assignments, Automation, Identification, Technology Uses in Education
Han Wan; Hongzhen Luo; Mengying Li; Xiaoyan Luo – IEEE Transactions on Learning Technologies, 2024
Automatic program repair (APR) tools are valuable for students to assist them with debugging tasks since program repair captures the code modification to make a buggy program pass the given test-suite. However, the process of manually generating catalogs of code modifications is intricate and time-consuming. This article proposes contextual error…
Descriptors: Programming, Computer Science Education, Introductory Courses, Assignments
Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
Pereira, Filipe Dwan; Rodrigues, Luiz; Henklain, Marcelo Henrique Oliveira; Freitas, Hermino; Oliveira, David Fernandes; Cristea, Alexandra I.; Carvalho, Leandro; Isotani, Seiji; Benedict, Aileen; Dorodchi, Mohsen; de Oliveira, Elaine Harada Teixeira – IEEE Transactions on Learning Technologies, 2023
Programming online judges (POJs) have been increasingly used in CS1 classes, as they allow students to practice and get quick feedback. For instructors, it is a useful tool for creating assignments and exams. However, selecting problems in POJs is time consuming. First, problems are generally not organized based on topics covered in the CS1…
Descriptors: Artificial Intelligence, Man Machine Systems, Educational Technology, Technology Uses in Education
Marwan, Samiha; Akram, Bita; Barnes, Tiffany; Price, Thomas W. – IEEE Transactions on Learning Technologies, 2022
Theories on learning show that formative feedback that is immediate, specific, corrective, and positive is essential to improve novice students' motivation and learning. However, most prior work on programming feedback focuses on highlighting student's mistakes, or detecting failed test cases after they submit a solution. In this article, we…
Descriptors: Feedback (Response), Formative Evaluation, Programming, Coding
Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Chad C. Tossell; Nathan L. Tenhundfeld; Ali Momen; Katrina Cooley; Ewart J. de Visser – IEEE Transactions on Learning Technologies, 2024
This article examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre-post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational…
Descriptors: Student Attitudes, Computer Software, Artificial Intelligence, Grading

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