Advertisement

A Socio-Semantic Network Analysis of Discourse Using the Network Lifetime and the Moving Stanza Window Method

  • Ayano OhsakiEmail author
  • Jun Oshima
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1112)

Abstract

This study proposes a new temporal Socio-Semantic Network Analysis (SSNA) of discourse by using the network lifetime and the moving stanza window method to analyze idea improvement in learning as knowledge-creation. The procedure of our proposed method has four steps. The first step entails making a discourse analysis unit. One discourse analysis unit is composed of discourses depending on the set numbers at a size of the moving stanza window method. The second step is calculating the total value of degree centrality for each discourse analysis unit with periods of the network lifetime by using SSNA. The third step involves calculating the difference value between discourse analysis units to define the candidates for the pivotal points. The last step is tracing the discourse back from the candidates for the pivotal points to identify segments for in-depth dialogical discourse analysis. To evaluate the proposed method, we analyzed discourse data in collaborative learning using different methods with and without the network lifetime and moving stanza window. As a result, new pivotal points were detected by implementing both the network lifetime and the moving stanza window method. An in-depth dialogical discourse analysis of a new pivotal discourse segment confirmed the appropriateness of the detection. Based on the results, it is concluded that our proposed method is better in detecting pivotal points of learning as knowledge-creation compared to the previous approach.

Keywords

Visualization Socio-Semantic Network Analysis Temporal analysis Discourse analysis 

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers 16H0187, JP18K13238, 19H01715.

References

  1. 1.
    Paavola, S., Lipponen, L., Hakkarainen, K.: Models of innovative knowledge communities and three metaphors of learning. Rev. Educ. Res. 74(4), 557–576 (2004)CrossRefGoogle Scholar
  2. 2.
    Oshima, J., Oshima, R., Matsuzawa, Y.: Knowledge building discourse explorer: a social network analysis application for knowledge building discourse. Educ. Technol. Res. Dev. 60, 903–921 (2012)CrossRefGoogle Scholar
  3. 3.
    Scardamalia, M., Bereiter, C.: Knowledge building and knowledge creation: theory, pedagogy, and technology. In: Sawyer, K. (ed.) The Cambridge Handbook of the Learning Sciences, 2nd edn, pp. 397–417. Cambridge University Press, New York (2014)CrossRefGoogle Scholar
  4. 4.
    Oshima, J., Oshima, R., Fujita, W.: A mixed-methods approach to analyze shared epistemic agency in jigsaw instruction at multiple scales of temporality. J. Learn. Anal. 5(1), 10–24 (2018)CrossRefGoogle Scholar
  5. 5.
    Dyke, G., Kumar, R., Ai, H., Rosé, C.P.: Challenging assumptions: using sliding window visualizations to reveal time-based irregularities in CSCL processes. In: Proceedings of the 10th ICLS, vol. 1, pp. 363–370 (2012)Google Scholar
  6. 6.
    Siebert-Evenstone, A.L., Irgens, G.A., Collier, W., Swiecki, Z., Ruis, A.R., Shaffer, D.W.: In search of conversational grain size: modeling semantic structure using moving stanza windows. J. Learn. Anal. 4(3), 123–139 (2017)CrossRefGoogle Scholar
  7. 7.
    Barabási, A.: Network Science. Cambridge University Press, Cambridge (2016)zbMATHGoogle Scholar
  8. 8.
    Barabási, A.: Bursts: The Hidden Patterns Behind Everything We Do, From Your E-mail to Bloody Crusades. Plume, New York (2011)Google Scholar
  9. 9.
    Oshima, J., Ohsaki, A., Yamada, Y., Oshima, R.: Collective knowledge advancement and conceptual understanding of complex scientific concepts in the jigsaw instruction. In: Smith, B.K., Borge, M., Mercier, E., Lim, K.Y. (eds.). Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, vol. 1, pp. 57–64 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Advanced Institute of Industrial TechnologyTokyoJapan
  2. 2.Research and Education Center for the Learning SciencesShizuoka UniversityShizuokaJapan

Personalised recommendations