NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 9 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Sohum M. Bhatt; Wim Van Den Noortgate; Katrien Verbert – IEEE Transactions on Learning Technologies, 2024
Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many…
Descriptors: Elementary Secondary Education, Artificial Intelligence, Educational Technology, Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Hani Y. Ayyoub; Omar S. Al-Kadi – IEEE Transactions on Learning Technologies, 2024
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that…
Descriptors: Cognitive Style, Individualized Instruction, Learning Management Systems, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
A. N. Varnavsky – IEEE Transactions on Learning Technologies, 2024
The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…
Descriptors: Hierarchical Linear Modeling, Intelligent Tutoring Systems, Individualized Instruction, Electronic Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Lixiang Xu; Zhanlong Wang; Suojuan Zhang; Xin Yuan; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT) is an intelligent educational technology used to model students' learning progress and mastery in adaptive learning environments for personalized education. Despite utilizing deep learning models in KT, current approaches often oversimplify students' exercise records into knowledge sequences, which fail to explore the rich…
Descriptors: Knowledge Level, Educational Technology, Intelligent Tutoring Systems, Individualized Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Huaiya Liu; Yuyue Zhang; Jiyou Jia – IEEE Transactions on Learning Technologies, 2024
Intelligent tutoring systems (ITSs) aim to deliver personalized learning support to each learner, aligning with the educational aspiration of many countries, including China. ITSs' personalized support is mainly achieved by providing individual prompts to learners when they encounter difficulties in problem-solving. The guiding principles and…
Descriptors: Intelligent Tutoring Systems, Mathematics Achievement, Individualized Instruction, Foreign Countries
Peer reviewed Peer reviewed
Direct linkDirect link
Xiang Wu; Huanhuan Wang; Yongting Zhang; Baowen Zou; Huaqing Hong – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate…
Descriptors: Artificial Intelligence, Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
Peer reviewed Peer reviewed
Direct linkDirect link
Tracy Bobko; Mikiko Corsette; Minjuan Wang; Erin Springer – IEEE Transactions on Learning Technologies, 2024
This article discusses the transformative impact of technology on knowledge acquisition and sharing, focusing on the emergence of the metaverse as a virtual community with vast potential for virtual learning. Learning in the metaverse is found to enhance engagement, motivation, and retention, while fostering 21st-century skills. It also offers…
Descriptors: Educational Innovation, Computer Simulation, Technology Uses in Education, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Xizhe Wang; Yihua Zhong; Changqin Huang; Xiaodi Huang – IEEE Transactions on Learning Technologies, 2024
Reading comprehension is a widely adopted method for learning English, involving reading articles and answering related questions. However, the reading comprehension training typically focuses on the skill level required for a standardized learning stage, without considering the impact of individual differences in linguistic competence. This…
Descriptors: Reading Comprehension, Artificial Intelligence, Computer Software, Synchronous Communication