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Yuqin Yang; Xueqi Feng; Gaoxia Zhu; Kui Xie – Journal of Computer Assisted Learning, 2024
Background: Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer-supported collaborative inquiry approaches, such as KB--the focus of this…
Descriptors: Undergraduate Students, Cooperative Learning, Epistemology, Inquiry
Baars, Martine; Viberg, Olga – International Journal of Mobile and Blended Learning, 2022
This paper discusses the possibilities of using and designing mobile technology for learning purposes coupled with learning analytics to support self-regulated learning (SRL). Being able to self-regulate one's own learning is important for academic success but is also challenging. Research has shown that without instructional support, students are…
Descriptors: Electronic Learning, Independent Study, Learning Analytics, Metacognition
Winne, Philip H. – Metacognition and Learning, 2022
Metacognition is the engine of self-regulated learning. At the object level, learners seek information and choose learning tactics and strategies they forecast will develop knowledge. At the meta level, learners gather and analyze data about learning events to draw conclusions, such as: Is this tactic a good fit to conditions? Was it effective?…
Descriptors: Metacognition, Learning Strategies, Computer Software, Data Analysis
Lingyun Huang; Juan Zheng; Susanne P. Lajoie; Yuxin Chen; Cindy E. Hmelo-Silver; Minhong Wang – Education and Information Technologies, 2024
Learning analytics dashboards (LADs) are often used to display real-time data indicating student learning trajectories and outcomes. Successful use of LADs requires teachers to orient their dashboard reviews with clear goals, apply appropriate strategies to interpret visualized information on LADs and monitor and evaluate their interpretations to…
Descriptors: Learning Analytics, Educational Technology, Self Management, Learning Strategies
Park, Eunsung; Ifenthaler, Dirk; Clariana, Roy B. – British Journal of Educational Technology, 2023
The real-time and granularized learning information and recommendations available from adaptive learning technology can provide learners with feedback that is personalized. However, at an individual level, learners often experience technological and pedagogical conflicts. Learners have more freedom to accept, ignore or reject the feedback while…
Descriptors: Metacognition, Learning Analytics, Learning Management Systems, Learning Strategies
Papamitsiou, Zacharoula; Economides, Anastasios A. – Journal of Computer Assisted Learning, 2021
This longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help--in the form of on-demand task-related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self-assessment activities, conducted at four discrete points in time,…
Descriptors: Metacognition, Help Seeking, Learning Analytics, Student Behavior
Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
Wang, Han; Huang, Tao; Tian, Jun; Yang, Huali; Han, Pengdong – Best Evidence in Chinese Education, 2022
In the age of Internet Plus, the deep integration of information technology into education and individualized instruction have become a growing trend in education development. Self-regulated learning is a key element of student core competence, but easy to be overlooked in basic education. The purpose of this study is to establish the data…
Descriptors: Elementary School Students, Scaffolding (Teaching Technique), Learning Strategies, Models
Gabbay, Hagit; Cohen, Anat – International Educational Data Mining Society, 2023
In MOOCs for programming, Automated Testing and Feedback (ATF) systems are frequently integrated, providing learners with immediate feedback on code assignments. The analysis of the large amounts of trace data collected by these systems may provide insights into learners' patterns of utilizing the automated feedback, which is crucial for the…
Descriptors: MOOCs, Feedback (Response), Teaching Methods, Learning Strategies
Lars de Vreugd; Anouschka van Leeuwen; Renée Jansen; Marieke van der Schaaf – Journal of Learning Analytics, 2024
For university students, self-regulation of study behaviour is important. However, students are not always capable of effective self-regulation. Providing study behaviour information via a learning analytics dashboard (LAD) may support phases within self-regulated learning (SRL). However, it is unclear what information a LAD should provide, how to…
Descriptors: Learning Management Systems, Learning Analytics, Student Behavior, Behavior Patterns
Joel Weijia Lai; Wei Qiu; Maung Thway; Lei Zhang; Nurabidah Binti Jamil; Chit Lin Su; Samuel S. H. Ng; Fun Siong Lim – Journal of Learning Analytics, 2025
The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot,…
Descriptors: Artificial Intelligence, Computer Software, Learning Analytics, Introductory Courses
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
Zhang, Jiayi; Andres, Juliana Ma. Alexandra L.; Hutt, Stephen; Baker, Ryan S.; Ocumpaugh, Jaclyn; Mills, Caitlin; Brooks, Jamiella; Sethuraman, Sheela; Young, Tyron – International Educational Data Mining Society, 2022
Self-regulated learning (SRL) is a critical component of mathematics problem solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model, proposes that…
Descriptors: Mathematics Instruction, Teaching Methods, Problem Solving, Metacognition
Chen, Li; Lu, Min; Goda, Yoshiko; Yamada, Masanori – International Association for Development of the Information Society, 2019
Metacognition is an aspect in self-regulated learning and is necessary to achieve such learning in an effective and efficient manner. However, it is not always easy and accurate for learners to monitor or assess their own metacognition. In this study, we designed a learning analytics dashboard to improve self-regulated learning in online…
Descriptors: Learning Analytics, Metacognition, Learning Strategies, Information Management
Viberg, Olga; Kukulska-Hulme, Agnes; Peeters, Ward – International Journal of Mobile and Blended Learning, 2023
Mobile-assisted language learning (MALL) research includes examination and development of second language learners' cognitive and metacognitive self-regulated learning skills, but the affective learning component of self-regulation in this context remains largely unexplored. Support for affective learning, which is defined by learners' beliefs,…
Descriptors: Metacognition, Computer Assisted Instruction, Second Language Learning, Second Language Instruction
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