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Data-Driven Design for Computer-Supported Collaborative Learning : Design Matters.
・ISBN 978-981-16-1717-1 hard EUR 179.99
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★★★
| 著者・編者 | Zheng, Lanqin, |
|---|---|
| シリーズ | Lecture Notes in Educational Technology |
| 出版社 | (Springer, GW) |
| 出版年月 | 2021.08 |
| ページ数 | 162 pp. |
| 言語 | ENG |
| ニュース番号 | <663-L3260 663-P4977> |
解説
This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research.
The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.
The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.