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Chunyun Zhang; Hebo Ma; Chaoran Cui; Yumo Yao; Weiran Xu; Yunfeng Zhang; Yuling Ma – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT) aims to trace students' evolving knowledge states based on their learning sequences. Recently, some deep learning based models have been proposed to incorporate the historical information of individuals to trace students' knowledge states and achieve encouraging progress. However, these works ignore the collaborative…
Descriptors: Supervision, Knowledge Level, Learning Processes, Cooperative Learning
Masaya Okada; Koryu Nagata; Nanae Watanabe; Masahiro Tada – IEEE Transactions on Learning Technologies, 2024
A learner can autonomously acquire knowledge by experiencing the world, without necessarily being explicitly taught. The contents and ways of this type of real-world learning are grounded on his/her surroundings and are self-determined by computing real-world information. However, conventional studies have not modeled, observed, or understood a…
Descriptors: Computation, Learning Analytics, Experiential Learning, Self Management
David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
Chen, Hong-Ren; Lin, Wen-Shan; Hsu, Tien-Yu; Lin, Tzu-Chun; Chen, Nian-Shing – IEEE Transactions on Learning Technologies, 2023
Research on the use of augmented reality technology in museums is mostly limited to scientific knowledge. The use of wearable device technology learning materials to benefit students in the process of English learning has been somewhat explored, along with differences in students' learning styles and their influence on students' learning…
Descriptors: Educational Games, Museums, Situated Learning, Cognitive Style
Galaige, Joy; Steele, Geraldine Torrisi; Binnewies, Sebastian; Wang, Kewen – IEEE Transactions on Learning Technologies, 2022
Student-facing learning analytics (SFLA) hold promise for supporting the development of self-regulated learning (SRL) skills students need for academic success, especially in online learning. However, the promise of SFLA for supporting SRL is unrealized because current SFLA design methods are technocentric, with little attention to learning…
Descriptors: Learning Analytics, Learning Strategies, Design, Student Needs
Esteban Villalobos; Mar Perez-Sanagustin; Roger Azevedo; Cedric Sanza; Julien Broisin – IEEE Transactions on Learning Technologies, 2024
Blended learning (BL) has become increasingly popular in higher education institutions. Despite its popularity and the advances in methodologies for the detection of learning tactics and strategies from trace data, little is known about how they apply to BL settings and, therefore, how students use them to plan, organize, monitor, and regulate…
Descriptors: Metacognition, Learning Strategies, Blended Learning, Instructional Design
Weijiao Huang; Khe Foon Hew – IEEE Transactions on Learning Technologies, 2025
In an online learning environment, both instruction and assessments take place virtually where students are primarily responsible for managing their own learning. This requires a high level of self-regulation from students. Many online students, however, lack self-regulation skills and are ill-prepared for autonomous learning, which can cause…
Descriptors: Independent Study, Interpersonal Relationship, Electronic Learning, Computer Software
Han, Feifei; Ellis, Robert A.; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2022
This article uses digital traces to help identify students' online learning strategies by making a clear distinction between the descriptive features (the proportional distribution of students' different online learning actions) and quantitative aspects (the total number of the online learning sessions), a distinction that has not been properly…
Descriptors: Electronic Learning, Learning Strategies, Student Behavior, Educational Environment
Dominguez, Cesar; Garcia-Izquierdo, Francisco J.; Jaime, Arturo; Perez, Beatriz; Rubio, Angel Luis; Zapata, Maria A. – IEEE Transactions on Learning Technologies, 2021
The study of the relationships between self-regulated learning and formative assessment is an active line of research in the educational community. A recent review of the literature highlights that the study of these connections has been mainly unidirectional, focusing on how formative assessment helps students to self-regulate their learning,…
Descriptors: Learning Analytics, Time Factors (Learning), Self Evaluation (Individuals), Formative Evaluation
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
Saint, John; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or…
Descriptors: Undergraduate Students, Engineering Education, Learning Analytics, Learning Strategies
Matcha, Wannisa; Uzir, Nora'ayu Ahmad; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics…
Descriptors: Learning Analytics, Computer Interfaces, Educational Research, Learning Strategies
Lin, Jian-Wei; Koong Lin, Hao-Chiang; Chen, Hong-Ren – IEEE Transactions on Learning Technologies, 2022
Conventional e-learning platforms require a high self-regulatory learning (SRL) ability to ensure learning effectiveness. However, because not everyone has high autonomy and a high SRL ability, many students quit during the online learning period. To enhance the SRL ability, many studies have developed e-learning platforms based on Zimmerman's SRL…
Descriptors: Metacognition, Role Models, Learning Strategies, Personal Autonomy
Elizabeth Koh; Lishan Zhang; Alwyn Vwen Yen Lee; Hongye Wang – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been…
Descriptors: Vocabulary, Visual Aids, Electronic Publishing, Word Frequency
Ellis, Robert A.; Han, Feifei; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Collaboration is an increasingly important and difficult skill for graduate engineers to develop. While universities provide some measures of collaboration ability of students on graduation, there is still some dissatisfaction with the level of preparedness of students for collaborative activity in the workplace. This paper presents a case study…
Descriptors: Engineering Education, College Freshmen, Blended Learning, Cooperation
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