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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
Xiaona Xia – Interactive Learning Environments, 2023
Effective analysis and demonstration of these data features is of great significance for the optimization of interactive learning environment and learning behavior. Therefore, we take the big data set of learning behavior generated by an online interactive learning environment as the research object, define the features of learning behavior, and…
Descriptors: Learning Strategies, Interaction, Educational Environment, Learning Analytics
Jelena N. Larsen; Kine M. D. Maxwell; Mohammad Khalil – Journal of Learning Analytics, 2025
Effective learning design (LD) grounded in sound pedagogy is a critical driver of student success. Therefore, it is important to explore how LD of online learning environments influences student ability to manage their own learning. This understanding can inform the development of online programs that prioritize student-driven learning. Research…
Descriptors: Literature Reviews, Learning Analytics, Instructional Design, Student Motivation
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
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
Fan, Yizhou; Tan, Yuanru; Rakovic, Mladen; Wang, Yeyu; Cai, Zhiqiang; Shaffer, David Williamson; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and…
Descriptors: MOOCs, Students, Learning Processes, Learning Strategies
Logan Sizemore; Brian Hutchinson; Emily Borda – Chemistry Education Research and Practice, 2024
Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student's organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student's strategy.…
Descriptors: Artificial Intelligence, Chemistry, Cognitive Ability, Abstract Reasoning
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

Conrad Borchers; Jeroen Ooge; Cindy Peng; Vincent Aleven – Grantee Submission, 2025
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Learner Controlled Instruction, Learning Analytics
Zhao, Fuzheng; Liu, Gi-Zen; Zhou, Juan; Yin, Chengjiu – Educational Technology & Society, 2023
Big data in education promotes access to the analysis of learning behavior, yielding many valuable analysis results. However, with obscure and insufficient guidelines commonly followed when applying the analysis results, it is difficult to translate information knowledge into actionable strategies for educational practices. This study aimed to…
Descriptors: Learning Analytics, Man Machine Systems, Artificial Intelligence, Learning Strategies
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
Allan Jeong; Hyoung Seok-Shin – International Association for Development of the Information Society, 2023
The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the…
Descriptors: Critical Thinking, Learning Strategies, Concept Mapping, Learning Analytics
Ignacio Villagrán; Rocio Hernández; Gregory Schuit; Andrés Neyem; Javiera Fuentes; Loreto Larrondo; Elisa Margozzini; María T. Hurtado; Zoe Iriarte; Constanza Miranda; Julián Varas; Isabel Hilliger – Journal of Learning Analytics, 2024
Remote technology has been widely incorporated into health professions education. For procedural skills training, effective feedback and reflection processes are required. Consequently, supporting a self-regulated learning (SRL) approach with learning analytics dashboards (LADs) has proven beneficial in online environments. Despite the potential…
Descriptors: Feedback (Response), Independent Study, Skill Development, Learning Analytics