Visualizing patient decision-making processes regarding choice of therapy with Epistemic Network Analysis: A worked example of manual coding and segmentation
Our research initiative aimed to explore patient decision-making concerning their choice of therapy: biomedicine, non-conventional medicine, or both. These decisions, occurring throughout the patient journey, are intricately tied to the patient’s previous experiences, their trusted sources of information, and what they think caused their illness. We employed Epistemic Network Analysis (ENA) as an analytical system that enabled us to handle large amounts of data and capture the systemic nature of many variables involved. Yet applying Quantitative Ethnography (QE) techniques to continuous narratives (e.g. semi-structured interviews) in an inquiry where manual segmentation with a multitude of codes is preferred poses several challenges. In order to address these issues, we developed the Reproducible Open Coding Kit (ROCK) – convention, open source software, and interface – that eases manual coding, enables researchers to reproduce the coding process, compare results, and collaborate. Our aim is to broaden the usage of QE, while facilitating Open Science principles and transparency. Our webinar will elaborate our research, address issues surrounding the QE treatment of continuous narratives, and introduce the basic functionality of the ROCK. We hope to see you there!
The influence of discipline on teachers’ knowledge and decision making
The knowledge required by teachers has long been a focus of public and academic attention. Following a period of intense research interest in teachers’ knowledge in the 1980’s and 1990’s, many researchers have adopted Shulman’s (1987) suggestion that expert teaching practice is based on seven forms of knowledge which collectively are referred to as a knowledge base for teaching. Shulman’s work also offered a decision-making framework known as pedagogical reasoning and action which allows teachers to use their seven forms of knowledge to make effective pedagogical decisions. Despite the widespread acceptance of these ideas, no empirical evidence exploring the connections between knowledge and decision-making are evident in the research literature. This paper reports on a pilot study in which the connections between knowledge and decisions in science, mathematics and information technology teachers’ lesson plans are quantified and represented using epistemic network analysis. Findings reveal and levels of complexity that have been intimated but, until now, not supported with empirical evidence.
A Linguistic Ethnographic Perspective on Classroom Identities and Participation (And Some Challenges for Quantitative Ethnography)
Most quantitative research on classroom discourse focuses on structural and cognitive dimensions of the interaction. For example, researchers have examined teacher questions, student argumentation, sequential structures, and the distribution of participation. For good reason: such variables are central to many of our conceptualizations of effective pedagogy, and they readily lend themselves to systematic observation and quantitative measurement. Nevertheless, more happens in classroom discourse and interaction than is captured in such measures. Students negotiate their own and one another's identities, make sense of lesson content and expectations, manage relationships with peers and teacher, struggle to assert their voices, and find creative ways of passing the time while also staying out of trouble. Likewise, teachers are occupied with managing these student concerns, classroom power relations, and institutional pressures, while also living up to institutional and ideological expectations. Linguistic ethnography offers a powerful set of tools for making sense of such forces and issues, which critically shape learning processes and outcomes. On the other hand, these tools are not well-suited to quantifying variables or working with large data sets. In this talk we will (a) provide a brief introduction to a linguistic ethnographic perspective on classroom discourse analysis; (b) demonstrate this perspective through the analysis of identity, peer relations and participation in a brief classroom episode; and (c) present some of our initial attempts to transform this object of inquiry into a set of variables that would allow us to engage in quantitative ethnographic analysis.
Using quantitative ethnography to tell stories that have not been told before
Where social and epistemic networks meet
In addition to the commonly used data sources about learners and their knowledge practices, data about learners’ social interactions have also attracted significant attention of learning analytics researchers. Social network analysis (SNA) emerged early as one of the cornerstones of the learning analytics research, providing the opportunity to automatically extract large-scale networks from learners' interactions across various environments, such as LMSs and different social media platforms. Epistemic network analysis (ENA), on the other hand, recently emerged as a technique to analyse coded data of individual and collaborative learning. ENA is a graph-based method for analysing associations between coded data and represents an operationalization of the learning science theory of epistemic frames. As such, these two methods represent complementary approaches that, combined, provide comprehensive understanding of knowledge processes at the individual and group level.
In this talk, we will review the strengths and opportunities that SNA and ENA provide methodologically for learning analytics. In so doing, we will evaluate various educational contexts and demonstrate the depth of analytical insights obtained when these two network-based approaches are utilised together. Finally, although both methods build on strong theoretical underpinnings, we will discuss the role of Connectivism as a theoretical base to further develop social and epistemic network signature – SENS, as coined by Gašević and colleagues.
We’re All in this Together: Modeling Interdependence in Collaborative Settings
When individuals collaborate their actions are not isolated. Instead, they respond to and build upon the actions of others, creating interdependent systems. This suggests that valid models of collaborative processes, which inform research, assessments, and education, should account for interdependence. However, many models treat the collaborative actions of individuals as independent from the context in which they occur. In this talk, I discuss an approach for determining the conditions under which models that account for interdependence are more appropriate. The approach estimates the difference between independent and interdependent models using information about the social and cognitive structure of the collaborative context. Using simulation studies, I show that the estimates are reliable under a variety of conditions. This work furthers our understanding of the social and cognitive interactions that characterize collaboration, and provides guidance for researchers as to which kind of model (independent vs interdependent) may be more appropriate for their data.