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Titlebook: Learning Analytics in Open and Distributed Learning; Potential and Challe Paul Prinsloo,Sharon Slade,Mohammad Khalil Book 2022 The Editor(s

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发表于 2025-3-21 18:27:02 | 显示全部楼层 |阅读模式
书目名称Learning Analytics in Open and Distributed Learning
副标题Potential and Challe
编辑Paul Prinsloo,Sharon Slade,Mohammad Khalil
视频videohttp://file.papertrans.cn/583/582685/582685.mp4
概述Examines the challenges and practices of learning analytics in open, distance and distributed learning environments.Expands on the history of theoretical and empirical research in distance education.O
丛书名称SpringerBriefs in Education
图书封面Titlebook: Learning Analytics in Open and Distributed Learning; Potential and Challe Paul Prinsloo,Sharon Slade,Mohammad Khalil Book 2022 The Editor(s
描述This book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the “data revolution” and the emergence of learning analytics on this increasingly diverse form of educational delivery. Following an introductory chapter that maps the book’s conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.
出版日期Book 2022
关键词Distance Learning; Distance Education; Open Distance Learning; Distributed Learning; Learning Analytics;
版次1
doihttps://doi.org/10.1007/978-981-19-0786-9
isbn_softcover978-981-19-0785-2
isbn_ebook978-981-19-0786-9Series ISSN 2211-1921 Series E-ISSN 2211-193X
issn_series 2211-1921
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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发表于 2025-3-21 23:20:24 | 显示全部楼层
Book 2022ctory chapter that maps the book’s conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.
发表于 2025-3-22 01:37:52 | 显示全部楼层
2211-1921 f theoretical and empirical research in distance education.OThis book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the “data revolution” and the emergence of learning analytics on this increasingl
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发表于 2025-3-22 16:01:27 | 显示全部楼层
A Global South Perspective on Learning Analytics in an Open Distance E-learning (ODeL) Institution,al of data from institutional databases (with the necessary permissions/ethical clearances) and present it in a useful manner to all the parties that require it. Finally, the chapter recommends a facility to enable effective data presentation for a range of stakeholders such as students, lecturers,
发表于 2025-3-22 19:32:02 | 显示全部楼层
Learning Analytics in Open and Distance Higher Education: The Case of the Open University UK,ion of approaches, such as predictive learning analytics, especially when there are limited resources to employ teachers to communicate with students and moderate timely interventions. This chapter concludes by noting the importance of human interaction to facilitate and achieve learning outcomes. L
发表于 2025-3-23 00:37:48 | 显示全部楼层
Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learnin/or evaluate learning analytics approaches for mobile learning activities for the purposes of measuring and fostering student SRL in diverse online learning environments. MOLAM is especially valuable for continuous measurement and interventions, thus fostering students’ transferable SRL skills, stra
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发表于 2025-3-23 09:34:20 | 显示全部楼层
Situating Learning Analytics for Course Design in Online Secondary Contexts,n illuminate design issues and reveal opportunities to change existing teaching and learning activities. The authors argues that the emphasis on smaller scale data analyses provides information relevant for identifying larger scale applications that can be constructed from explorations of more local
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