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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Imane Nedjar; Mohammed M'hamedi – Education and Information Technologies, 2024
Tailored support is crucial for deaf and hearing-impaired children to overcome learning difficulties, particularly during primary education. The absence of listening profoundly hinders the progression of the learning journey, as it plays a pivotal role in language acquisition. Employing assistive technology is one approach to address this issue in…
Descriptors: Deafness, Sign Language, Arabic, Artificial Intelligence
Ridvan Elmas; Merve Adiguzel-Ulutas; Mehmet Yilmaz – Education and Information Technologies, 2024
Many people use technological tools that are widely accessible, respond quickly, and have extensive information networks today. Due to recent technological advances in education and the increasing acceptance of Artificial Intelligence (AI) technologies, the issues regarding their implementation in education require identification and analysis.…
Descriptors: Artificial Intelligence, Science Education, Biochemistry, Information Dissemination
Ayse Alkan; Ezgi Pelin Yildiz – International Journal of Research in Education and Science, 2024
The main goal of this study is to reveal special talented primary school students' perceptions of artificial intelligence, one of the popular concepts of recent times, through metaphors. In this study, the phenomenological design, which is within the scope of qualitative research, was used. In this study, Türkiye Science and Art Center included…
Descriptors: Foreign Countries, Gifted, Elementary School Students, Middle School Students
Thulasi M. Santhi; K. Srinivasan – IEEE Transactions on Learning Technologies, 2024
Cloud adoption in industrial sectors, such as process, manufacturing, health care, and finance, is steadily rising, but as it grows, the risk of targeted cyberattacks has increased. Hence, effectively defending against such attacks necessitates skilled cybersecurity professionals. Traditional human-based cyber-physical education is resource…
Descriptors: Artificial Intelligence, Information Security, Computer Security, Prevention
Chandan Kumar Tiwari; Mohd. Abass Bhat; Shagufta Tariq Khan; Rajaswaminathan Subramaniam; Mohammad Atif Irshad Khan – Interactive Technology and Smart Education, 2024
Purpose: The purpose of this paper is to identify the factors determining students' attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model. Design/methodology/approach: The recommended model was empirically…
Descriptors: Foreign Countries, College Students, Artificial Intelligence, Student Attitudes
Bryan Abendschein; Xialing Lin; Chad Edwards; Autumn Edwards; Varun Rijhwani – Journal of Computer Assisted Learning, 2024
Background: Education is often the primary arena for exploring and integrating new technologies. AI and human-machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education. Objectives: We sought to understand student perceptions of credibility related to written…
Descriptors: Students, Student Attitudes, Feedback (Response), Writing (Composition)
Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
Andrew Rejan – English Journal, 2024
Close reading, as Paula Moya (2016) writes, may remain the "most powerful discipline-specific tool we have at our disposal" (p. 9). At a time when the work of English teachers is threatened by many factors, including political polarization and the rise of artificial intelligence (AI), close reading may be the soundest way of unifying and…
Descriptors: Critical Reading, English Instruction, Educational History, Literary Criticism
Javiera Atenasa; Leo Havemannb; Chrissi Nerantzi – Research in Learning Technology, 2024
This paper offers guidance on employing open and creative methods for co- designing critical data and artificial intelligence (AI) literacy spaces and learning activities, rooted in the principles of Data Justice. Through innovative approaches, we aim to enhance participation in learning, research and policymaking, fostering a comprehensive…
Descriptors: Artificial Intelligence, Data, Literacy, Teaching Methods
Jian Zhao; Elaine Chapman; Peyman G. P. Sabet – Education Research and Perspectives, 2024
The launch of ChatGPT and the rapid proliferation of generative AI (GenAI) have brought transformative changes to education, particularly in the field of assessment. This has prompted a fundamental rethinking of traditional assessment practices, presenting both opportunities and challenges in evaluating student learning. While numerous studies…
Descriptors: Literature Reviews, Artificial Intelligence, Evaluation Methods, Student Evaluation
Amal Abdullah Alibrahim – South African Journal of Education, 2024
After ChatGPT was released late in 2022, many arguments about its accuracy and use in education arose. In this article, I seek to provide evidence of the accuracy and validity of ChatGPT's responses to users' queries in education by applying a systematic review methodology to analyse publications in specific databases following PRISMA guidelines…
Descriptors: Artificial Intelligence, Technology Uses in Education, Reliability, Natural Language Processing
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
Xuelin Liu; Hua Zhang; Yue Cheng – International Journal of Web-Based Learning and Teaching Technologies, 2024
In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the…
Descriptors: Artificial Intelligence, Data, Text Structure, Classification
Dan Shen; Wenjia Zhao – International Journal of Web-Based Learning and Teaching Technologies, 2024
With the development of internet technology, big data has been used to evaluate the singing and pronunciation quality of vocal students. However, current methods have several problems such as poor information fusion efficiency, low algorithm robustness, and low recognition accuracy under low signal-to-noise ratio. To address these issues, this…
Descriptors: Data, Music Education, Pronunciation, Singing

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