#1340: TopoLA Dashboard: Making Topological Learning Analytics Accessible for Exploring Learner Behavioral Structure

Topological Data Analysis (TDA) offers powerful methods for understanding structural patterns in learner populations, yet its application in learning analytics remains an emerging field with limited interpretive precedent. This interactive demo presents TopoLA Dashboard, a tool designed to make topological learning analytics accessible to educators and researchers without requiring topology expertise. By applying Zigzag Persistent Homology to behavioral data from the Open University Learning Analytics Dataset, the dashboard tracks β_0—the number of connected behavioral components—across the course timeline, alongside composite indicators TEWI and TSI. We connect these constructs explicitly to CSCL concepts including social cohesion and co-regulated learning, while foregrounding a critical interpretive distinction: apparent β_0 convergence may reflect learner attrition rather than genuine behavioral alignment. Rather than providing definitive interpretations, the dashboard employs a three-layer framework—objective metrics, research-grounded pattern descriptions, and reflective prompts—to support educator sensemaking. We illustrate this framework with an authentic OULAD course example, demonstrating how rising β_0 and TEWI values in the weeks preceding an assessment deadline can prompt targeted instructional reflection. Participants will explore authentic OULAD data, experiment with topological parameters, and discuss design implications for emerging structural analytics.