A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data

Authors

  • David Williamson Shaffer
  • Wesley Collier
  • A. R. Ruis

DOI:

https://doi.org/10.18608/jla.2016.33.3

Abstract

This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks—the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking—ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.

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Published

2016-12-19

How to Cite

Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data. Journal of Learning Analytics, 3(3), 9-45. https://doi.org/10.18608/jla.2016.33.3

Issue

Vol. 3 No. 3 (2016): Learning Analytics Tutorials

Section

Special section: Tutorials in learning analytics (LASI and LAK 2014)

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