Deep making: Curricular modules for transferable content-knowledge and scientific literacy in makerspaces and FabLabs

Yoav Bergner, Ofer Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe work in progress on Deep Making, a framework for the design of curricular modules that facilitate learning of transferable content-knowledge in makerspaces and FabLabs. Concern about epistemological dilution and our experience in co-design work with educators in these settings has informed this theoretical framing. We discuss the need for such a set of modules and the educational theories that guide us in the process of designing them. We present a few examples of such modules and sketch out a research design to test their efficacy.

Original languageEnglish (US)
Title of host publicationIDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children
PublisherAssociation for Computing Machinery, Inc
Pages551-556
Number of pages6
ISBN (Electronic)9781450351522
DOIs
StatePublished - Jun 19 2018
Event17th ACM Conference on Interaction Design and Children, IDC 2018 - Trondheim, Norway
Duration: Jun 19 2018Jun 22 2018

Publication series

NameIDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children

Other

Other17th ACM Conference on Interaction Design and Children, IDC 2018
CountryNorway
CityTrondheim
Period6/19/186/22/18

Keywords

  • Constructionism
  • Curriculum design
  • FabLabs
  • Makerspaces
  • Scientific literacy
  • Technological literacy

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Education
  • Software
  • Human-Computer Interaction

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  • Cite this

    Bergner, Y., & Chen, O. (2018). Deep making: Curricular modules for transferable content-knowledge and scientific literacy in makerspaces and FabLabs. In IDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children (pp. 551-556). (IDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children). Association for Computing Machinery, Inc. https://doi.org/10.1145/3202185.3210774