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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
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
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
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
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
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
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
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
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
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
Tzeng, Jian-Wei; Lee, Chia-An; Huang, Nen-Fu; Huang, Hao-Hsuan; Lai, Chin-Feng – International Review of Research in Open and Distributed Learning, 2022
Massive open online courses (MOOCs) are open access, Web-based courses that enroll thousands of students. MOOCs deliver content through recorded video lectures, online readings, assessments, and both student-student and student-instructor interactions. Course designers have attempted to evaluate the experiences of MOOC participants, though due to…
Descriptors: Online Courses, Models, Learning Analytics, Artificial Intelligence
Jui-Hung Chang; Chi-Jane Wang; Hua-Xu Zhong; Hsiu-Chen Weng; Yu-Kai Zhou; Hoe-Yuan Ong; Chin-Feng Lai – Educational Technology Research and Development, 2024
Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…
Descriptors: Artificial Intelligence, Technological Advancement, Student Evaluation, Models
Blankenship, Rebecca J. – Distance Learning, 2023
The use of existing and emerging technologies in teaching modalities and learning spaces provides the opportunity to present subject-area content using devices, programs, and modalities in more authentic ways that promote higher order thinking and promote long-term concept retention. In the last decade, advances in artificial intelligence (AI)…
Descriptors: Technological Literacy, Pedagogical Content Knowledge, Self Concept, Scaffolding (Teaching Technique)
K. Keerthi Jain; J. N. V. Raghuram – Education and Information Technologies, 2024
This research delves into the multifaceted landscape of various factors that influence the adoption of Generation-Artificial Intelligence (Gen-AI) in Higher Education. By employing a comprehensive framework that includes perceived risk, perceived ease of use, usefulness, Technological Pedagogical Content Knowledge (TPACK), and trust, the study…
Descriptors: Prediction, Artificial Intelligence, Technological Literacy, Pedagogical Content Knowledge
Magsayo, Roche Tumlad – International Journal of Information and Learning Technology, 2021
Purpose: The study aims to determine the factor of perceived machine learning adoption (MLA) values that affect learners' intention to continue using (ICU), the extent of their relationships in the learners' ICU and the role of locus of control (LOC) in their relationship. Design/methodology/approach: The study employed a rigorous literature…
Descriptors: Foreign Countries, Rural Schools, Higher Education, Artificial Intelligence
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