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Studying interactions faces methodological challenges and existing methods, such as configural diagramming, have limitations. This work demonstrates Epistemic Network Analysis (ENA) as an analytical method to construct configural diagrams. We demonstrated ENA as an analytical tool by applying this method to study dementia caregiver work systems. We conducted 20 semistructured interviews with caregivers to collect caregiving experiences. Guided by the Patient Work System model, we conducted a directed content analysis to identify work system components and used ENA to study interactions between components. By using ENA to create configural diagrams, we identified five frequently occurring interactions, compared work system configurations of caregivers providing care at home and away from home. Although we were underpowered to determine statistically significant differences, we identified visual and qualitative differences. Our results demonstrate the capability of ENA as 

When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists. 

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. 

Quantitative ethnographers across a range of domains study complex collaborative thinking (CCT): the processes by which members of a group or team develop shared understanding by making cognitive connections from the statements and actions of the group. CCT is difficult to model because the actions of group members are interdependent—the activity of any individual is influenced by the actions of other members of the group. Moreover, the actions of group members engaged in some collaborative tasks may need to follow a particular order. However, current techniques can account for either interdependence or order, but not both. In this paper, we present directed epistemic network analysis (dENA), an extension of epistemic network analysis (ENA), as a method that can simultaneously account for the interdependent and ordered aspects of CCT. To illustrate the method, we compare a qualitative analysis of two U.S. Navy commanders working in a simulation to ENA and dENA analyses of their performance. We find that by accounting for interdependence but not order, ENA was not able to model differences between the commanders seen in the qualitative analysis, but by accounting for both interdependence and order, dENA was able to do so.