Data-Driven Design for Computer-Supported Collaborative Learning (eBook)

Dr. Lanqin Zheng currently works as an associate professor as well as supervisors for international PHD students and postgraduate students at the School of Educational Technology in Beijing Normal University. Her research interests include computer supported collaborative learning, learning analytics, instructional design, and STEM. Dr. Zheng has published more than 100 refereed journal papers, books, and international conference papers. Now, she serves as the editorial board member of International Journal of Mobile Leaning and Organization as well as assistant editors of Journal of Computers in Education and Smart Learning Environment. She is the reviewers of many international journals, including Review of Educational Research, Computers & Education, The Internet and Higher Education, British Journal of Educational Technology, and so on. She is also program committee members of many international conferences.

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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.