Digital tools have the ability to log the fine-grained details of user experiences within and across the system. These digital experiences can lend valuable contextualization to other ethnographic insights. In this paper, we discuss the potential for using interaction logs as a data source and the pipeline considerations that can facilitate and enhance quantitative ethnographic research using this type of data. We draw on previous QE work and examples from QE adjacent fields such as educational data mining, learning analytics, and human-computer interaction to provide evidence for this approach.
The Change the Museum Instagram account launched in June 2020 to document instances of unchecked racism in US museums. When the posts are coded based on the prevailing identity categories of race, gender identity, sexual orientation, and positions of power, among other constructs, an Epistemic Network Analysis (ENA) model can be generated for each in order to visualize connections between individuals and the structural and systemic factors contributing to harmful work environments. The methodology of this study expands previous analyses of the first month and initial six months of posts by considering a full year of social media data. One goal in assessing this set of utterances is to contribute to policy change plans and decisions in this culture sector. Another aim is to demonstrate the shared, intersectional encounters with racism and other injustices as a roadmap for meaningful change.
Intersectional identity theory, also known as intersectionality, asserts that an individual’s unique combination of identities both mediates and is mediated by their sociocultural contexts, and is thus a valuable theoretical framework for exploring identity. However, while intersectionality has been largely applied within USA contexts to explore intersections of race, gender, and class, its applicability to non-USA contexts remains largely unexplored. We interviewed high school students in the USA and Pakistan to understand their conceptualizations of intersectional identities with regard to their broader life experiences as well as within the context of their educational experiences. In this paper, we analyze ten of these interviews through a quantitative ethnographic-epistemic network analysis (QE-ENA) approach to identify connections made by participants between specific aspects of identity within their conceptualizations. Our results suggest that participants from different country groups emphasize and connect different aspects of identity and point towards the need to better contextualize intersectional identity theory for non-Western global contexts, alongside broader implications for applications of intersectionality within further global research as well as the efficacious nature of QE-ENA to analyze issues of intersectionality.
Many learning technologies are now able to support both user-customization of the content and automated personalization of the experience based on user activities. However, there is a tradeoff between customization and personalization: the more control an educator or learner has over the parameters that define the experience, the more difficult it is to develop learning analytic models that can reliably assess learning and adapt the system accordingly. In this paper, we present a novel QE method for automatically generating a learning analytic model for the land-use planning simulation iPlan, which enables users to construct custom local simulations of socio-environmental issues. Specifically, this method employs data simulation and network analysis to construct a measurement space using nothing but log data. This space can be used to analyze users' problem-solving processes in a context where normative measurement criteria cannot be specified in advance. In doing so, we argue that QE methods can be developed and employed even in the absence of rich qualitative data, facilitating thick(er) descriptions of complex processes based on relatively thin records of users' activities in digital systems.
This paper explores the experiences of preservice teachers of color completing teacher preparation historically white university. Drawing on critical race theory (CRT), this paper foregrounds intersectionality of participants’ various and intersecting identities, using epistemic network analysis (ENA) to represent connections between participants’ identities and senses of belonging within the context of their programs. ENA presents a unique opportunity to visually represent the connections between participants’ experiences and multiple salient identities simultaneously, minimizing the risk of essentialism and tokenization. Results of this intersectional analysis of preservice teachers of colors’ (PSTOC) experiences and implications for teacher education suggest that PSTOC experiences of belonging in teacher preparation can be understood through program interactions and that PSTOC social identities influence the ways they approach and make sense of their experiences. Further implications for the utility of ENA as a tool for understanding identity and intersectional experiences are offered.
