Date:
April 28th, 2026
Time:
1:30 PM - 5:00 PM
Room:
Dræggen 3
This half-day workshop introduces participants to advanced applications of Quantitative Ethnography (QE) in the context of AI-augmented learning environments. Building on previous LAK workshops, it highlights how QE weaves together the depth of qualitative insights and the breadth of quantitative patterns to make sense of complex learning data shaped by AI systems. Participants will gain hands-on experience with established and emerging QE tools, including Epistemic Network Analysis (ENA), Ordered Network Analysis (ONA), and Transmodal Analysis (TMA), alongside Codey, a new LLM-powered tool for automated and validated coding. Designed for both newcomers and intermediate users, the session combines theoretical grounding with practical exploration of how QE supports theory-driven, multimodal, and process-oriented learning analytics. Through guided activities and collaborative discussions, attendees will strengthen their methodological toolkit for analyzing human–AI interactions and interpreting learning processes with rigor, transparency, and contextual richness.
1:30 - 1:45 | Welcome |
---|---|
1:45 – 02:45 | Introduction to Quantitative Ethnography: Foundation sand Toolset Overview |
02:45 - 03:00 | Coffee Break |
03:00 - 03:50 | Group Work I: Participants choose from ENA, ONA, TMA, and Codey |
03:50 - 04:40 | Group Work II: Participants choose from ENA, ONA, TMA, and Codey |
04:40 - 05:00 | Further Q&A and Reflections |
Zachari Swiecki
Monash University,
Australia
Kamila Misiejuk
FernUniversität in Hagen, Germany
Rogers Kaliisa
University of Oslo, Norway
Mamta Shah
Elsevier,
United States
Brendan Eagan
UW Madison,
United States
Yuanru Tan
UW Madison,
United States
Cody Marquart
UW Madison,
United States