Open AccessA Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data
Author(s) -
David Williamson Shaffer,
W. A. L. Collier,
Andrew R. Ruis
Publication year - 2016
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2016.33.3
Subject(s) - computer science , association (psychology) , social network analysis , set (abstract data type) , data science , range (aeronautics) , network science , network analysis , cognition , artificial intelligence , data mining , machine learning , complex network , psychology , social media , materials science , neuroscience , world wide web , composite material , psychotherapist , programming language , physics , quantum mechanics
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.