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

Autor/inn/en	Shaffer, David Williamson; Collier, Wesley; Ruis, A. R.
Titel	A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data
Quelle	In: Journal of Learning Analytics, 3 (2016) 3, S.9-45 (37 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Leitfaden; Epistemology; Network Analysis; Data Analysis; Coding; Models; Evaluation Methods; Robustness (Statistics); Visualization; Cognitive Processes; Social Networks; Metadata; Matrices; Correlation + Suchen Sie Ihr Suchwort? Erkenntnistheorie; Netzplantechnik; Auswertung; Codierung; Programmierung; Analogiemodell; Widerstandsfähigkeit; Visualisation; Visualisierung; Cognitive process; Kognitiver Prozess; Social network; Soziales Netzwerk; Metadaten; Matrizenrechnung; Korrelation
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. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01