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Elahe Allahyari – ProQuest LLC, 2024
This work explores the complex cognitive processes students engage in when addressing contextual tasks requiring linear and exponential models. Grounded within Piagetian constructivism and the Knowledge in Pieces (KiP) epistemological perspective (diSessa, 1993, 2018), this empirical study in a clinical setting develops a Microgenetic Learning…
Descriptors: Learning Analytics, Abstract Reasoning, Mathematical Models, Algebra
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Emine Cabi; Hacer Türkoglu – International Review of Research in Open and Distributed Learning, 2025
Recent advancements in educational technology have enabled teachers to use learning analytics (LA) and flipped classrooms. The present study investigated the impact of a LA-based feedback system on students' academic achievement and self-regulated learning (SRL) in a flipped learning (FL) environment. The study used a pretest-posttest control…
Descriptors: Learning Analytics, Feedback (Response), Academic Achievement, Independent Study
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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
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Belle Dang; Andy Nguyen; Sanna Järvelä – Journal of Learning Analytics, 2024
Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these…
Descriptors: Learning Analytics, Cooperative Learning, Artificial Intelligence, Epistemology
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Xing, Wanli; Zhu, Gaoxia; Arslan, Okan; Shim, Jaesub; Popov, Vitaliy – Journal of Computing in Higher Education, 2023
Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement…
Descriptors: Learning Analytics, Cooperative Learning, Learner Engagement, Learning Motivation
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Yuqin Yang; Yewen Chen; Xueqi Feng; Daner Sun; Shiyan Pang – Journal of Computing in Higher Education, 2024
Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in…
Descriptors: Undergraduate Students, Learning Processes, Learning Analytics, Learner Engagement
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Xinyu Li; Yizhou Fan; Tongguang Li; Mladen Rakovic; Shaveen Singh; Joep van der Graaf; Lyn Lim; Johanna Moore; Inge Molenaar; Maria Bannert; Dragan Gaševic – Journal of Learning Analytics, 2025
The focus of education is increasingly on learners' ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to…
Descriptors: Learning Analytics, Metacognition, Independent Study, Skill Development
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Karaoglan Yilmaz, Fatma Gizem – Journal of Computing in Higher Education, 2022
This research examined the effect of learning analytics (LA) on students' metacognitive awareness and academic achievement in an online learning environment. In this study, a mixed methods approach was used and applied as a quasi-experimental design. The results of LA were sent to students weekly in LA group (experimental group) via learning…
Descriptors: Learning Analytics, Feedback (Response), Metacognition, Academic Achievement
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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
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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
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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
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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
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Han, Insook; Obeid, Iyad; Greco, Devon – Technology, Knowledge and Learning, 2023
This report describes the use of electroencephalography (EEG) to collect online learners' physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new…
Descriptors: Learning Analytics, Diagnostic Tests, Metacognition, Brain Hemisphere Functions
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Damien S. Fleur; Max Marshall; Miguel Pieters; Natasa Brouwer; Gerrit Oomens; Angelos Konstantinidis; Koos Winnips; Sylvia Moes; Wouter van den Bos; Bert Bredeweg; Erwin A. van Vliet – Journal of Learning Analytics, 2023
Personalized feedback is important for the learning process, but it is time consuming and particularly problematic in large-scale courses. While automatic feedback may help for self-regulated learning, not all forms of feedback are effective. Social comparison offers powerful feedback but is often loosely designed. We propose that intertwining…
Descriptors: Feedback (Response), Peer Influence, Learning Analytics, Undergraduate Students
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Zheng, Lanqin; Kinshuk; Fan, Yunchao; Long, Miaolang – Education and Information Technologies, 2023
Online collaborative learning has been an effective pedagogy in the field of education. However, productive collaborative learning cannot occur spontaneously. Learners often have difficulties in collaborative knowledge building, group performance, coregulated behaviors, learning engagement, and social interaction. To promote productive…
Descriptors: Learning Analytics, Performance, Electronic Learning, Cooperative Learning
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