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Showing 1 to 15 of 32 results Save | Export
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Nasrin Dehbozorgi; Mourya Teja Kunuku – IEEE Transactions on Education, 2024
Contribution: An AI model for speech emotion recognition (SER) in the educational domain to analyze the correlation between students' emotions, discussed topics in teams, and academic performance. Background: Research suggests that positive emotions are associated with better academic performance. On the other hand, negative emotions have a…
Descriptors: Interaction, Academic Achievement, Artificial Intelligence, Psychological Patterns
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Jiang, Shiyan; Tang, Hengtao; Tatar, Cansu; Rosé, Carolyn P.; Chao, Jie – Learning, Media and Technology, 2023
It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through…
Descriptors: Artificial Intelligence, High School Students, Models, Classification
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Md Al Amin; Yang Sok Kim; Mijin Noh – Education and Information Technologies, 2025
The introduction of artificial intelligence technologies like ChatGPT has brought a revolution in various sectors, including higher education. The study aims to examine the drivers that influence ChatGPT adoption among students in higher studies in Bangladesh. This study combined UTAUT model components with constructs such as perceived knowledge…
Descriptors: Trust (Psychology), Artificial Intelligence, Computer Software, Social Influences
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Zhao, Anping; Yu, Yu – IEEE Transactions on Learning Technologies, 2022
To provide insight into online learners' interests in various knowledge from course discussion texts, modeling learners' sentiments and interests at different granularities are of great importance. In this article, the proposed framework combines a deep convolutional neural network and a hierarchical topic model to discover the hidden structure of…
Descriptors: Online Courses, Student Attitudes, Knowledge Level, Networks
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Rashid, M. Parvez; Xiao, Yunkai; Gehringer, Edward F. – International Educational Data Mining Society, 2022
Peer assessment can be a more effective pedagogical method when reviewers provide quality feedback. But what makes feedback helpful to reviewees? Other studies have identified quality feedback as focusing on detecting problems, providing suggestions, or pointing out where changes need to be made. However, it is important to seek students'…
Descriptors: Peer Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
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Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
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Yüregilli Göksu, Derya; Duran, Volkan – International Journal of Curriculum and Instruction, 2023
The aim of the study is the examination of the flipped classroom approach used in foreign language lessons on the opinions of gifted students in the context of bibliometric analysis of the literature and the analysis of GPT-3 model chatbot. In this study, the descriptive method was used. It is aimed to obtain in-depth information according to…
Descriptors: Flipped Classroom, Bibliometrics, Artificial Intelligence, Models
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Rand Al-Dmour; Hani Al-Dmour; Yazeed Al-Dmour; Ahmed Al-Dmour – Journal of International Students, 2025
In this study, we examine the role of AI-driven marketing in international student recruitment, focusing on how perceived usefulness, trust, and personalization influence decision-making. Grounded in the Technology Acceptance Model (TAM), the Trust-Based Decision-Making Model, and the Personalization--Privacy Paradox, we studied how AI-powered…
Descriptors: Foreign Students, Student Recruitment, Trust (Psychology), Privacy
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Chen-Chen Liu; Hai-Jie Wang; Dan Wang; Yun-Fang Tu; Gwo-Jen Hwang; Youmei Wang – Interactive Learning Environments, 2024
Teachers' instructional design skills influence their teaching practices and student learning performances. However, researchers have found that the traditional one-to-many model of preservice teacher education prevents preservice teachers from receiving timely and individualized feedback, making it difficult to fill in theoretical knowledge gaps…
Descriptors: Preservice Teachers, Instructional Design, Teaching Skills, Knowledge Level
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Jian-Hong Ye; Mengmeng Zhang; Weiguaju Nong; Li Wang; Xiantong Yang – Education and Information Technologies, 2025
ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also…
Descriptors: Artificial Intelligence, College Students, Problem Solving, Student Attitudes
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Dolawattha, Dhammika Manjula; Premadasa, H. K. Salinda; Jayaweera, Prasad M. – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of this study is to evaluate the sustainability of the proposed mobile learning framework for higher education. Most sustainability evaluation studies use quantitative and qualitative methods with statistical approaches. Sometimes, in previous studies, machine learning models were utilized conventionally.…
Descriptors: Sustainability, Higher Education, Artificial Intelligence, Electronic Learning
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Oluwanife Segun Falebita; Petrus Jacobus Kok – Journal for STEM Education Research, 2025
This study investigates the relationship between undergraduates' technological readiness, self-efficacy, attitude, and usage of artificial intelligence (AI) tools. The study leverages the technology acceptance model (TAM) to explore the relationships among the study's variables. The study's participants are 176 undergraduate students from a public…
Descriptors: Artificial Intelligence, Technology Uses in Education, Structural Equation Models, Undergraduate Students
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D. V. D. S. Abeysinghe; M. S. D. Fernando – IAFOR Journal of Education, 2024
"Education is the key to success," one of the most heard motivational statements by all of us. People engage in education at different phases of our lives in various forms. Among them, university education plays a vital role in our academic and professional lives. During university education many undergraduates will face several…
Descriptors: Models, At Risk Students, Mentors, Undergraduate Students
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Chanaa, Abdessamad; El Faddouli, Nour-eddine – International Journal of Information and Communication Technology Education, 2022
Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented…
Descriptors: MOOCs, Student Attitudes, Data Analysis, Electronic Learning
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Manar Hazaimeh; Abdullah M. Al-Ansi – International Journal of Information and Learning Technology, 2024
Purpose: Artificial intelligence (AI) is constantly evolving and is poised to significantly transform the world, affecting nearly every sector and aspect of society. As AI continues to evolve, it is expected to create a more dynamic, efficient and personalized education system, supporting lifelong learning and adapting to the needs and pace of…
Descriptors: Artificial Intelligence, Adoption (Ideas), Teacher Attitudes, Student Attitudes
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