Refining qualitative ethnographies using Epistemic Network Analysis: A study of socioemotional learning dimensions in a Humanistic Knowledge Building Community

Yotam Hod, Shir Katz, Brendan Eagan

Research output: Contribution to journalArticlepeer-review

Abstract

Contemporary educational research has increasingly pointed to socioemotional dimensions of learning as important in promoting academic progress and sociocognitive developments. Epistemic Network Analysis, a methodology for producing quantitative ethnographies based on complex learning environments, has only begun to examine socioemotional facets of learning in classrooms. The aim of this research is to investigate what and how Epistemic Network Analysis can contribute to qualitative, socioemotionally-focused ethnographies of classroom learning communities. To do this, we employed Epistemic Network Analysis to analyze data collected during a semester of studies, in parallel to a stage developmental analysis of the same community using qualitative methods. The results of this study specifically show the importance of prior experience and how this interacts with participants' connectedness to the community, as well as how group dynamics are a vital aspect of community discourse and that the socioemotional dimensions that people attach to it may be the determinants of stage advancement. More generally, this study shows how Epistemic Network Analysis can be used to better understand complex socioemotional phenomena in learning communities by combining it with deep, qualitative ethnographies.

Original languageEnglish
Article number103943
JournalComputers and Education
Volume156
DOIs
StatePublished - Oct 2020

Bibliographical note

Funding Information:
This research was supported by the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation grant 1716/12. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 796045. We would like to thank Michal Dvir and Chen Ya'ari for their help with the data analysis.

Funding Information:
This research was supported by the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation grant 1716/12. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 796045 . We would like to thank Michal Dvir and Chen Ya'ari for their help with the data analysis.

Publisher Copyright:
© 2020

Keywords

  • Cooperative/collaborative learning
  • Data science applications in education
  • Learning communities
  • Pedagogical issues
  • Post-secondary education

ASJC Scopus subject areas

  • Computer Science (all)
  • Education

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