Approaching Structured Debate with Quantitative Ethnography in Mind

Abstract

Structured Debate (SD) is a constrained discourse style that is popular in many different forums. The expansion of SD to online platforms leaves many questions about addressing this type of data during analysis. Quantitative Ethnography (QE) may provide a framework for the considerations that need to be made when analyzing SD datasets. In this paper, we review the ways in which QE methods are compatible with SD and the challenges associated with applying this method. Using data from an online, SD forum, we present a narrative of decision-making throughout the analysis process. We find that QE allows for a myriad of insights to be gained from this form of data depending on the approach one takes including insights into structures, content, and participation. This work intends to serve as a model for researchers hoping to utilize QE on SD and, more broadly, for approaching novel datasets.

Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis

“…The key affordances of QE methods are that they can capture (a) complex dependencies between feature-rich data sets and (b) yield interpretable insights for practitioners that scaffold interpretation processes that would otherwise rely on statistical expertise. While traditionally relying on codes from qualitative discourse, QE studies have utilized log data collected from digital tools to understand fine-grain learning processes, for example, to situate log traces with video or text replays to interpret the context of human-system interactions, enabling rich descriptions of learning at a large scale [34].…”

“…The present study leverages network methods to fill this gap in understanding the interplay of in-tutor learning and classroom practices. Epistemic Network Analysis (ENA) [35] and Ordered Network Analysis (ONA) [38] in quantitative ethnography (QE) [35] receive increasing attention in learning analytics and promise interpretable insight into learning processes [34]. These methods encode temporal relationships between virtual and physical events and establish ties of which behaviors frequently co-occur (e.g., student disengagement and teacher monitoring).…”