Learning and knowledge exchange in science teaching

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

This poster presents our study of how social networks support learning and knowledge exchange among participants of a professional development program for science teachers. Social networks data and qualitative interviews were used to assess participants' current learning networks as well as their perceptions, practices, and experiences with their current learning environment and innovation pertaining to science teaching and learning. Results indicate that while the majority of the connections were confined to their school buildings, teachers acquire a considerable amount of new knowledge from networks outside these buildings. The interviews revealed a common desire among participants to foster new connections and talk to others about science teaching and learning, and show the important role of information from outside the school as contributing to new activities. The interviews also provide insight into the kinds of interactions that bond the teachers with their colleagues and the kinds of learning that are given or received in science teachers' everyday practice.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 iConference
Subtitle of host publicationInspiration, Integrity, and Intrepidity, iConference 2011
Pages676-678
Number of pages3
DOIs
StatePublished - 2011
Externally publishedYes
Event6th Annual Conference on 2011 iConference: Inspiration, Integrity, and Intrepidity, iConference 2011 - Seattle, WA, United States
Duration: Feb 8 2011Feb 11 2011

Publication series

NameACM International Conference Proceeding Series

Other

Other6th Annual Conference on 2011 iConference: Inspiration, Integrity, and Intrepidity, iConference 2011
Country/TerritoryUnited States
CitySeattle, WA
Period2/8/112/11/11

Keywords

  • Knowledge exchange
  • Qualitative interviews
  • Social networks

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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