Data Literacies and Social Justice: Exploring Critical Data Literacies through Sociocultural Perspectives

The ability to interpret, evaluate, and make data-based decisions is critical in the age of big data. Normative scripts around the use of data position them as a privileged epistemic form conferring authority through objectivity that can serve as a lever for effecting change. However, humans and materials shape how data are created and used which can reinscribe existing power relations in society at large (Van Wart, Lanouette & Parikh, in press). Thus, research is needed on how learners can be supported to engage in critical data literacies through sociocultural perspectives. As a field intimately concerned with data-based reasoning, social justice, and design, the learning sciences is well-positioned to contribute to such an effort. This symposium brings together scholars to present theoretical frameworks and empirical studies on the design of learning spaces for critical data literacies. This collection supports a larger discussion around existing tensions, additional design considerations, and new methodologies

The Mathematical Foundations of Epistemic Network Analysis

Epistemic network analysis (ENA) has been used in more than 300 published studies to date. However, there is no work in publication that describes the transformations that constitute ENA in formal mathematical terms. This paper provides such a description, focusing on the mathematical formulations that lead to two key affordances of ENA that are not present in other network analysis tools or multivariate analyses: (1) summary statistics that can be used to compare the differences in the content rather than the structure of networks and (2) network visualizations that provide information that is mathematically consistent with those statistics. Specifically, we describe the mathematical transformations by which ENA constructs matrix representations of discourse data, uses those representations to generate networks for units of analysis, places those networks into a metric space, identifies meaningful dimensions in the space, and positions the nodes of network graphs within that space so as to enable interpretation of those dimensions in terms of the content of the networks. We conclude with a discussion of how the mathematical formalisms of ENA can be used to model networks more generally

Scoping the Emerging Field of Quantitative Ethnography: Opportunities, Challenges and Future Directions

Quantitative Ethnography (QE) is an emerging methodological approach that combines ethnographic and statistical tools to analyze both Big Data and smaller data to study human behavior and interactions. This paper presents a methodological scoping review of 60 studies employing QE approaches with an intention to characterize and establish where the boundaries of QE might and should be in order to establish the identity of the field. The key finding is that QE researchers have enough commonality in their approach to the analysis of human behavior with a strong focus on grounded analysis, the validity of codes and consistency between quantitative models and qualitative analysis. Nonetheless, in order to reach a larger audience, the QE community should attend to a number of conceptual and methodological issues (e.g. interpretability). We believe that the strength of work from individual researchers reported in this review and initiatives such as the recently established International Society for Quantitative Ethnography (ISQE) can present a powerful force to shape the identity of the QE communit