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Showing 1 to 15 of 23 results Save | Export
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Sha, Lele; Rakovic, Mladen; Lin, Jionghao; Guan, Quanlong; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Chen, Guanliang – IEEE Transactions on Learning Technologies, 2023
In online courses, discussion forums play a key role in enhancing student interaction with peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to students in a timely manner. To address this problem, both traditional machine learning (ML) (e.g., Random Forest) and deep learning (DL) approaches have been…
Descriptors: Computer Mediated Communication, Discussion Groups, Artificial Intelligence, Intelligent Tutoring Systems
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Lu, Dong – International Journal of Music Education, 2023
Interactive learning environments have become important components for teaching pianists, which determines the relevance of the research topic. The study was conducted in Faculty of Piano of the Shenyang Conservatory of Music, and 87 first-year undergraduates participated in it. A positive effect of interactive teaching aids on professional skills…
Descriptors: Music Education, Teaching Methods, Musical Instruments, Undergraduate Students
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Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2022
Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains,…
Descriptors: Student Projects, Feedback (Response), Natural Language Processing, Guidelines
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Bunyamin Celik; Yunus Yildiz; Saban Kara – Australian Journal of Applied Linguistics, 2025
Expressing thoughts and feelings efficiently is fundamental in daily, academic, and professional life. Accordingly, self-efficacy beliefs play pivotal roles in shaping learners' speaking performance through various dimensions. Higher education institutions assume responsibility for boosting students' speaking self-efficacy, thereby contributing to…
Descriptors: Self Efficacy, English (Second Language), Second Language Instruction, Second Language Learning
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Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – International Journal of Artificial Intelligence in Education, 2025
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
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Yung-Hsiang Hu; Jo Shan Fu; Hui-Chin Yeh – Interactive Learning Environments, 2024
Artificial intelligence aims to restructure and process re-engineering education and teaching processes and accelerate the evolution of the whole education system from information to intelligence. Robotic Process Automation (RPA) robots learn by observing people at work, analyzing user processes repeatedly, and adjusting or correcting automated…
Descriptors: Intelligent Tutoring Systems, Robotics, Automation, Instructional Effectiveness
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Vannaprathip, Narumol; Haddawy, Peter; Schultheis, Holger; Suebnukarn, Siriwan – International Journal of Artificial Intelligence in Education, 2022
Virtual reality simulation has had a significant impact on training of psychomotor surgical skills, yet there is still a lack of work on its use to teach surgical decision making. This is particularly noteworthy given the recognized importance of decision making in achieving positive surgical outcomes. With the objective of filling this gap, we…
Descriptors: Intelligent Tutoring Systems, Decision Making, Surgery, Teaching Methods
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Singla, Adish; Theodoropoulos, Nikitas – International Educational Data Mining Society, 2022
Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting struggling students, and need…
Descriptors: Programming, Computer Science Education, Task Analysis, Introductory Courses
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Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu – Interactive Learning Environments, 2023
Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in…
Descriptors: Comparative Analysis, Teaching Methods, Intelligent Tutoring Systems, Foreign Countries
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Lijuan Feng – Journal of Educational Computing Research, 2025
This study investigates the impact of AI-assisted language learning (AIAL) strategies on cognitive load and learning outcomes in the context of language acquisition. Specifically, the study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Second Language Learning, Second Language Instruction
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Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
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Tack, Anaïs; Piech, Chris – International Educational Data Mining Society, 2022
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…
Descriptors: Artificial Intelligence, Dialogs (Language), Bayesian Statistics, Decision Making
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Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
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Jost, Patrick – International Association for Development of the Information Society, 2021
Educators are increasingly confronted with technology-driven learning scenarios. Even before the push from the current pandemic, digital learning apps became an integrated didactic tool. Advanced computing can thereby support the digital content creation for educational courses offered on mobile platforms. Computed media content such as natural…
Descriptors: Artificial Intelligence, Computer Software, Nonverbal Communication, Decision Making
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Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis
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