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Direct linkFatima Abu Deeb; Timothy Hickey – Computer Science Education, 2024
Background and Context: Auto-graders are praised by novice students learning to program, as they provide them with automatic feedback about their problem-solving process. However, some students often make random changes when they have errors in their code, without engaging in deliberate thinking about the cause of the error. Objective: To…
Descriptors: Reflection, Automation, Grading, Novices
Download full textSaida Ulfa; Ence Surahman; Izzul Fatawi; Hirashima Tsukasa – Electronic Journal of e-Learning, 2024
The purpose of this study was to evaluate the factors that influence behavioural intention (BI) to use the Online Summary-with Automated Feedback (OSAF) in a MOOCs platform. Task-Technology Fit (TTF) was the main framework used to analyse the match between task requirements and technology characteristics, predictng the utilisation of the…
Descriptors: MOOCs, Intention, Automation, Feedback (Response)
Jakob Schwerter; Taiga Brahm – Technology, Knowledge and Learning, 2024
University students often learn statistics in large classes, and in such learning environments, students face an exceptionally high risk of failure. One reason for this is students' frequent statistics anxiety. This study shows how students can be supported using e-learning exercises with automated knowledge of correct response feedback,…
Descriptors: Statistics Education, College Students, Mathematics Anxiety, Electronic Learning
Jingjing Chen; Bing Xu; Dan Zhang – Educational Technology Research and Development, 2024
Learning-related attention is one of the most important factors influencing learning. Although technologies have enabled the automatic detection of students' attention levels, previous studies mainly focused on colleges or high schools, lacking further validations in primary school students. More importantly, the detected attention might fail to…
Descriptors: Elementary School Students, Attention, Attention Span, Learning Strategies
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
Zhe Zhang; Ling Xu – Journal of Multilingual and Multicultural Development, 2024
Aided by big-data technology and artificial intelligence, automated writing evaluation (AWE) systems aim to help students engage in self-regulated learning and improve their academic writing in the digital era. While much research on student engagement with AWE systems has been conducted in mainstream classrooms, little attention has been paid to…
Descriptors: Learner Engagement, Feedback (Response), Automation, Student Evaluation
