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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書目名稱Learning Analytics in Open and Distributed Learning影響因子(影響力)




書目名稱Learning Analytics in Open and Distributed Learning影響因子(影響力)學科排名




書目名稱Learning Analytics in Open and Distributed Learning網(wǎng)絡公開度




書目名稱Learning Analytics in Open and Distributed Learning網(wǎng)絡公開度學科排名




書目名稱Learning Analytics in Open and Distributed Learning被引頻次




書目名稱Learning Analytics in Open and Distributed Learning被引頻次學科排名




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書目名稱Learning Analytics in Open and Distributed Learning年度引用學科排名




書目名稱Learning Analytics in Open and Distributed Learning讀者反饋




書目名稱Learning Analytics in Open and Distributed Learning讀者反饋學科排名

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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.

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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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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,

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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

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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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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