Weather-it Missions: A Social Network Analysis Perspective of an Online Citizen Inquiry Community

  • Maria AristeidouEmail author
  • Eileen Scanlon
  • Mike Sharples
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)


Citizen inquiry is an innovative informal science learning approach, which engages members of the general public in scientific investigations sparked by their personal experience of everyday science, and to which other members can contribute. This paper aims to describe the network of interactions and contributions of Weather-it, an online Citizen Inquiry community accommodated by the nQuire-it platform, which involves people in creating and maintaining their own weather missions (investigations). The interaction patterns within Weather-it are mainly explored through social network analysis of community members and missions. The results indicate the quiet and active members within the community, their splitting into sub-communities, and their contribution and data collection methods and preferences. These results provide insight into the behaviour of people in such public engagement projects.


Citizen inquiry Engagement with science Online community Weather investigations Social interactions Social network analysis 


  1. 1.
    de Jong, T.: Technological advances in inquiry learning. Science 312(5773), 532–533 (2006)CrossRefGoogle Scholar
  2. 2.
    Aristeidou, M., Scanlon, E., Sharples, M.: A design-based study of Citizen Inquiry for geology. In: Katherine, M., Tomaž, K. (eds.) Proceeding of the Doctoral Consortium at the European Conference on Technology Enhanced Learning co-located with the EC-TEL 2013 conference, pp. 7–13. CEUR (2013)Google Scholar
  3. 3.
    Jordan, R., Crall, A., Gray, S., Phillips, T., Mellor, D.: Citizen Science as a Distinct Field of Inquiry. BioScience 65, 208–211 (2015)CrossRefGoogle Scholar
  4. 4.
    Nov, O., Arazy, O., Anderson, D.: Technology-mediated citizen science participation: a motivational model. In: Proceedings of the AAAI International Conference on Weblogs and Social Media (ICWSM 2011), pp. 249–256 (2011)Google Scholar
  5. 5.
    Raddick, M.J., Bracey, G., Gay, P.L., Lintott, C.J., Murray, P., Schawinski, K., Szalay, A.S., Vandenberg, J.: Galaxy zoo: exploring the motivations of citizen science volunteers. Astron. Educ. Rev. 9(1), 101–103 (2010)CrossRefGoogle Scholar
  6. 6.
    Cooper, C.B., Shirk, J., Zuckerberg, B.: The invisible prevalence of citizen science in global research: migratory birds and climate change. PloS ONE 9(9), e106508 (2014)CrossRefGoogle Scholar
  7. 7.
    Herodotou, C., Villasclaras-Fernández, E., Sharples, M.: Scaffolding citizen inquiry science learning through the nQuire toolkit. In: Proceedings of EARLI SIG 20: Computer Supported Inquiry Learning, pp. 9–11. Malmö, Sweden (2014)Google Scholar
  8. 8.
    Haythornthwaite, C., de Laat, M.: Social networks and learning networks: using social network perspectives to understand social learning. In: 7th International Conference on Networked Learning. Aalborg, Denmark (2010)Google Scholar
  9. 9.
    Pham, M.C., Derntl, M., Cao, Y., Klamma, R.: Learning analytics for learning blogospheres. In: Popescu, E., Li, Q., Klamma, R., Leung, H., Specht, M. (eds.) ICWL 2012. LNCS, vol. 7558, pp. 258–267. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  11. 11.
    Jordan, K.: Exploring co-studied Massive Open Online Course Subjects via social network analysis. Int. J. Emerg. Technol. Learn. 9(8), 38–41 (2014)CrossRefGoogle Scholar
  12. 12.
    Rabbany, R., Elatia, S., Takaffoli, M., Zaïane, O.R.: Collaborative learning of students in online discussion forums: A social network analysis perspective. In: Peña-Ayala, A. (ed.) Educational Data Mining, Studies in Computational Intelligence. Studies in Computational Intelligence, vol. 524, pp. 441–466. Springer International Publishing, Berlin (2014)Google Scholar
  13. 13.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 10, P10008 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maria Aristeidou
    • 1
    Email author
  • Eileen Scanlon
    • 1
  • Mike Sharples
    • 1
  1. 1.Institute of Educational TechnologyThe Open UniversityMilton KeynesUK

Personalised recommendations