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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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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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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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Amy Goodman; Youngjin Lee; Willard Elieson; Gerald Knezek – Journal of Computers in Mathematics and Science Teaching, 2023
Virtual learning environments give students more autonomy over their learning than traditional face-to-face classes and require that students adapt the ways they consume and assimilate new information. One theory of this process is self-regulated learning, which is illustrated in Efklides' Metacognitive and Affective model of Self-Regulated…
Descriptors: Self Management, Learning Theories, Learning Analytics, Undergraduate Students
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Karaoglan Yilmaz, Fatma Gizem; Yilmaz, Ramazan – Technology, Knowledge and Learning, 2022
One of the main problems encountered in the online learning process is the low or absence of students' engagement. They may face problems with behavioral engagement, cognitive engagement, emotional engagement in online learning environments. It is thought that the problems related to students' engagements can be overcome with personalized…
Descriptors: Learning Analytics, Intervention, Learner Engagement, Electronic Learning
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Karaoglan Yilmaz, Fatma Gizem; Yilmaz, Ramazan – Innovations in Education and Teaching International, 2021
In this research, the effect of the use of learning analytics (LA) based feedback as a metacognitive tool on the learners' transactional distance and motivation was examined. The research was carried out according to experimental design and was carried out on 81 university students. The students were randomly assigned to the experimental and…
Descriptors: Learning Analytics, Metacognition, Student Motivation, College Freshmen
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Chen, Li; Lu, Min; Goda, Yoshiko; Shimada, Atsushi; Yamada, Masanori – International Association for Development of the Information Society, 2020
In this study, we used a learning analytics dashboard (LAD) in a higher education course to support students' metacognition and evaluated the effects of its use. The LAD displays students' reading path and specific behaviors when viewing digital learning materials. The study was conducted on 53 university students to identify the factors that…
Descriptors: College Students, Learning Analytics, Metacognition, Educational Technology
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Volungeviciene, Airina; Duart, Josep Maria; Naujokaitiene, Justina; Tamoliune, Giedre; Rita Misiuliene, – Journal of Educators Online, 2019
The research aims at a specific analysis of how learning analytics as a metacognitive tool can be used as a method by teachers as reflective professionals and how it can help teachers learn to think and come down to decisions about learning design and curriculum, learning and teaching process, and its success. Not only does it build on previous…
Descriptors: Learning Analytics, Reflective Teaching, Metacognition, Decision Making
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Kokoç, Mehmet; Kara, Mehmet – Educational Technology & Society, 2021
The purposes of the two studies reported in this research are to adapt and validate the instrument of the Evaluation Framework for Learning Analytics (EFLA) for learners into the Turkish context, and to examine how metacognitive and behavioral factors predict learner performance. Study 1 was conducted with 83 online learners enrolled in a 16-week…
Descriptors: Learning Analytics, Electronic Learning, Measures (Individuals), Test Validity
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Viberg, Olga; Wasson, Barbara; Kukulska-Hulme, Agnes – Australasian Journal of Educational Technology, 2020
Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need…
Descriptors: Computer Assisted Instruction, Second Language Instruction, Second Language Learning, Lifelong Learning
Bull, Susan – Research and Practice in Technology Enhanced Learning, 2016
Today's technology-enabled learning environments are becoming quite different from those of a few years ago, with the increased processing power as well as a wider range of educational tools. This situation produces more data, which can be fed back into the learning process. Open learner models have already been investigated as tools to promote…
Descriptors: Educational Technology, Electronic Learning, Models, Computer Assisted Instruction