Forming Socio-intellectual Capital: The Case of LINKS

  • Daphne R. RabanEmail author
  • Dorit Geifman
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 17)


This chapter traces the formation of socio-intellectual value by the LINKS research center, which focuses on studying the co-creation of knowledge in spontaneous and designed learning environments, in the community at large and at educational facilities. By analyzing academic publications before and during LINKS activity using topic modeling, we identify evolving research areas. Topic modeling is a statistical algorithm which extracts the underlying thematic structure of a collection of documents, automatically identifying latent topical structures within the documents.

We received 447 articles published by 15 LINKS PIs from 2009 to 2016. The corpus for analysis consisted of the title, keywords, and abstracts of these articles. Three of the pre-LINKS topics persisted during LINKS were: Content Exchange, Gamification & Virtual Reality, and STEM Education. We observed a shift in focus from Curriculum Design, Online Communities, and Diversity Online pre-LINKS to Collaborative Learning, Learning Communities, and an increased interest in STEM Education thereafter.


Socio-intellectual capital Co-creation of knowledge Spontaneous and designed learning environments Topic modeling Content exchange Gamification Virtual reality STEM education Collaborative learning Learning communities 


  1. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.CrossRefGoogle Scholar
  2. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.CrossRefGoogle Scholar
  3. DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics, 41(6), 570–606.CrossRefGoogle Scholar
  4. Hornik, K., & Grün, B. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1–30.Google Scholar
  5. Hull, D. A. (1996). Stemming algorithms: A case study for detailed evaluation. JASIS, 47(1), 70–84.CrossRefGoogle Scholar
  6. Kali, Y., Tabak, I., Ben-Zvi, D., Kidron, A., Amzaleg, M., Baram-Tsabari, A., et al. (2015). Technology-enhanced learning communities on a continuum between ambient to designed: What can we learn by synthesizing multiple research perspectives. In The 11th international conference on the Computer Supported Collaborative Learning (CSCL) (Vol. 1, pp. 615–622).Google Scholar
  7. Mo, G. Y., Hayat, Z., & Wellman, B. (2015). How far can scholarly networks go? examining the relationships between distance, disciplines, motivations, and clusters. In Communication and information technologies annual (pp. 107–133). Bingley, UK: Emerald Group Publishing.CrossRefGoogle Scholar
  8. Raban, D. R., Gordon, A., & Geifman, D. (2011). The information society: The development of a scientific specialty. Information, Communication & Society, 14(3), 375–399.CrossRefGoogle Scholar
  9. Wellman, B., Dimitrova, D., Hayat, Z., Mo, G. Y., & Smale, L. (2014). Networking scholars in a networked organization. In Contemporary perspectives on organizational social networks (pp. 479–497). Bingley, UK: Emerald Group Publishing.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Haifa, Department of Business AdministrationHaifaIsrael

Personalised recommendations