Workshop Description

March 18, 2024 | 9:00 - 17:00

This workshop explores Quantitative Ethnography (QE) as a framework for supporting learning analytics in the age of Artificial Intelligence (AI). In many learning contexts, we increasingly have access to rich process data. To make meaning of this evidence, our goal is to develop a qualitatively “thick” description of the data and, thus, of learning. However, the more data we have, the more difficult this process becomes: qualitative analysis becomes less feasible, and quantitative analysis becomes less reliable. QE addresses this problem by using statistical techniques to warrant claims about the quality of thick descriptions. The result is a more unified mixed-methods approach that uniquely links the evidence we collect to learning processes and outcomes. This workshop focuses on different quantitative ethnography techniques that address this challenge, including Epistemic Network Analysis and Knowledge Building Discourse Explorer. The aim of the workshop is to examine these techniques and show how they can be combined to generate a more unified methodology for modeling learning processes and providing actionable insights for research and teaching practices. In addition to showcasing different analysis methods, this workshop includes a presentation of different data coding techniques, including qualitative, AI-supported, and other machine learning methods.

During the full-day workshop:

1. All participants will learn about the QE methodology and foundations.
2. All participants will explore a variety of approaches to data coding and data preprocessing, including AI-supported methods, to prepare different kinds of data for further analysis.
3. All participants will engage in a hands-on workshop introducing them to the basic principles and applications of ENA webtool, rENA, and KBDeX.
4. All participants will be assigned to small groups based on their expressed interest in exploring alternate analytic strategies and discussing the grounding of network approaches to learning analytics in QE.
5. At the end of the workshop, participants will present the main points of the discussions in small groups, which will form the basis for a white paper on QE and learning analytics.
   

There is also the possibility of a social gathering following the workshop organized by a local Japanese host.

Agenda

Facilitators

Jun Oshima
Shizuoka University, Japan

Kamila Misiejuk
University of Bergen, Norway

Rogers Kaliisa
University of Oslo, Norway

Jennifer Sciana
UW Madison,
United States

Zach Swiecki
Monash University, Australia

Brendan Eagan
UW Madison,
United States

Yeyu Wang
UW Madison,
United States