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Showing 1 to 15 of 171 results Save | Export
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Nedime Selin Çöpgeven; Mehmet Firat – Journal of Educators Online, 2024
Learning processes can now be transferred to digital environments, allowing for the tracking of learners' digital footprints. The field of learning analytics focuses on the efficient use of these digital records to improve both learning experiences and processes. Dashboards are the tangible outputs of learning analytics. The use of dashboards in…
Descriptors: Electronic Learning, Distance Education, Academic Achievement, Educational Technology
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Megan N. Imundo; Siyuan Li; Jiachen Gong; Andrew Potter; Tracy Arner; Danielle S. McNamara – Grantee Submission, 2025
Personalized learning (PL) is a student-centered instructional approach in which learning goals, pacing, content, and environments are customized to address individual student needs (Bernacki et al., 2021; Ellis, 2009; Lee, 2014; Miliband, 2006; Office of Educational Technology, 2010; Sota, 2016; Zhang et al., 2020). In grades K-12, PL has been…
Descriptors: Self Determination, Individualized Instruction, Electronic Learning, Higher Education
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Christothea Herodotou; Sagun Shrestha; Catherine Comfort; Heshan Andrews; Paul Mulholland; Vaclav Bayer; Claire Maguire; John Lee; Miriam Fernandez – Journal of Learning Analytics, 2025
In this paper, we explore the design of a student-facing dashboard for online and distance learning with a focus on capturing and addressing specific learning needs. A participatory process involving 20 students was employed, which included a screening questionnaire and focus group discussions. The selection of data points to be displayed on the…
Descriptors: Electronic Learning, Distance Education, Student Attitudes, Educational Technology
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Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
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Raymond A. Opoku; Bo Pei; Wanli Xing – Journal of Learning Analytics, 2025
While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…
Descriptors: Prediction, Accuracy, Electronic Learning, Artificial Intelligence
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Anuradha Peramunugamage; Uditha W. Ratnayake; Shironica P. Karunanayaka; Ellen Francine Barbosa; William Simão de Deus; Chulantha L. Jayawardena; R. K. J. de Silva – Journal of Learning for Development, 2025
Interactions among students in online learning environments are difficult to monitor but can be crucial for their academic performance. Moodle is one of the best and most popular online learning platforms, where its log records can reveal important information on students' engagement and the respective performance. This study examines the degree…
Descriptors: Cooperative Learning, Interaction, Electronic Learning, Learning Management Systems
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Anil Harun Kiliç; Serkan Izmirli – Asian Journal of Distance Education, 2024
This study conducted a systematic literature review of articles on learning analytics published between 2004 and January 2024. A total of 1,064 articles, identified using the keyword "learning analytic*" in the Scopus database, were analyzed. The study integrated systematic literature review and bibliometric analysis approaches to…
Descriptors: Literature Reviews, Learning Analytics, Foreign Countries, Data Use
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Elissavet Papageorgiou; Jacqueline Wong; Mohammad Khalil; Annoesjka J. Cabo – Journal of Learning Analytics, 2025
Behavioural engagement as a predictor of academic success hinges on the interplay between effort and time. Exploring the longitudinal development of engagement is vital for understanding adaptations in learning behaviour and informing educational interventions. However, person-oriented longitudinal studies on student engagement are scarce.…
Descriptors: Learner Engagement, Student Behavior, Electronic Learning, Web Based Instruction
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Zamecnik, Andrew; Kovanovíc, Vitomir; Joksimovíc, Srécko; Grossmann, Georg; Ladjal, Djazia; Marshall, Ruth; Pardo, Abelardo – Journal of Computer Assisted Learning, 2023
Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work-integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal…
Descriptors: Data Use, Group Dynamics, College Students, Cooperative Learning
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Hatice Yildiz Durak – Education and Information Technologies, 2025
Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing…
Descriptors: Feedback (Response), Online Systems, Electronic Learning, Learning Analytics
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Yiqiu Zhou; Jina Kang; Yeyu Wang; Muhammad Ashiq – Educational Technology Research and Development, 2025
The complex processes of collaborative knowledge construction require a multimodal approach to capture the interplay between learners, tools, and the environment. While existing studies have recognized the importance of considering multiple modalities, there remains a need for a comprehensive framework that explicitly models the dynamics of…
Descriptors: Cooperative Learning, Computer Simulation, Astronomy, Network Analysis
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Zheng, Lanqin; Zhong, Lu; Fan, Yunchao – Education and Information Technologies, 2023
Online collaborative learning (OCL) has been a mainstream pedagogy in the field of higher education. However, learners often produce off-topic information and engage less during online collaborative learning compared to other approaches. In addition, learners often cannot converge in knowledge, and they often do not know how to coregulate with…
Descriptors: Electronic Learning, Cooperative Learning, Undergraduate Students, Learning Analytics
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Muhittin Sahin – Interactive Learning Environments, 2024
Learning analytics aims to improve learning and teaching in digital learning environments by optimizing them. Real-time feedback, suggestions, directions, and interventions are structured in the digital learning environments. In order to structure more effective interventions, it is crucial to ascertain which of these services offered to students…
Descriptors: Learning Analytics, Intervention, Student Attitudes, Preferences
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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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Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
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