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Infrastructuring Distributed Studio Networks: A Case Study and Design Principles

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Title: Infrastructuring Distributed Studio Networks: A Case Study and Design Principles
Language: English
Authors: Smirnov, Natalia, Easterday, Matthew W., Gerber, Elizabeth M.
Source: Journal of the Learning Sciences. 2018 27(4):580-631.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 52
Publication Date: 2018
Sponsoring Agency: National Science Foundation (NSF), Division of Information and Intelligent Systems (IIS)
Contract Number: IIS1320693
IIS1530833
IIS1217225
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Descriptors: Interpersonal Relationship, Studio Art, Design, Social Media, Communities of Practice, Networks, Electronic Learning, Educational Environment, Educational Technology, College Students, Universities
DOI: 10.1080/10508406.2017.1409119
ISSN: 1050-8406
Abstract: Design educators have long used studio-based learning environments to create communities of learners to support authentic learning in design. Online social media platforms have enabled the creation of distributed studio networks (DSNs) that link studio-based learning environments into expanded communities of practice and potential networked improvement communities. As learning scientists, we do not adequately understand how to infrastructure learning and resource sharing across distributed studios. In this ethnography of the infrastructure of Design for America, a DSN, we analyzed data from interviews, online communication, and field observations as the organization grew its network of university design studios. We found that Design for America managers faced challenges of providing support and resources to address wide variation in needs across studios. Lacking an existing comprehensive network collaboration platform, managers created a proto-infrastructure to distribute support across studios. By studying their iterative adoption of communication and collaboration tools and organizational routines, we define a unique set of design principles to infrastructure DSNs: (a) surfacing local progress and problems, (b) affective crowding, (c) solution mapping, and (d) help routing. Assembling constellations of tools and designing platforms based on these principles could support learning in and the improvement of DSNs across domains.
Abstractor: As Provided
Number of References: 79
Entry Date: 2018
Accession Number: EJ1193909
Database: ERIC
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Design educators have long used studio-based learning environments to create communities of learners to support authentic learning in design. Online social media platforms have enabled the creation of distributed studio networks (DSNs) that link studio-based learning environments into expanded communities of practice and potential networked improvement communities. As learning scientists, we do not adequately understand how to infrastructure learning and resource sharing across distributed studios. In this ethnography of the infrastructure of Design for America, a DSN, we analyzed data from interviews, online communication, and field observations as the organization grew its network of university design studios. We found that Design for America managers faced challenges of providing support and resources to address wide variation in needs across studios. Lacking an existing comprehensive network collaboration platform, managers created a proto-infrastructure to distribute support across studios. By studying their iterative adoption of communication and collaboration tools and organizational routines, we define a unique set of design principles to infrastructure DSNs: (a) surfacing local progress and problems, (b) affective crowding, (c) solution mapping, and (d) help routing. Assembling constellations of tools and designing platforms based on these principles could support learning in and the improvement of DSNs across domains.
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