计算机科学
联想(心理学)
社会网络分析
集合(抽象数据类型)
数据科学
航程(航空)
网络科学
网络分析
认知
人工智能
数据挖掘
机器学习
复杂网络
心理学
社会化媒体
神经科学
复合材料
万维网
材料科学
程序设计语言
物理
心理治疗师
量子力学
作者
David Williamson Shaffer,Wesley Collier,A. R. Ruis
出处
期刊:Journal of learning Analytics
日期:2016-12-19
卷期号:3 (3): 9-45
被引量:474
标识
DOI:10.18608/jla.2016.33.3
摘要
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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