Temporal Networks
Static network snapshots collapse time and obscure the evolving nature of social interactions. Temporal networks preserve the chronological ordering of ties, making it possible to study how collaborative relationships form, strengthen, dissolve, and re-emerge over the course of a learning experience. I have been at the forefront of introducing temporal network analysis to education, demonstrating that time-resolved representations of student interactions reveal dynamics that aggregated views simply cannot capture.
My work has shown that temporal networks can serve as early predictors of academic performance, expose differential patterns of collaboration across platforms (e.g., synchronous vs. asynchronous), and illuminate the pathways of knowledge construction in problem-based learning. By combining temporal network analysis with methods such as process mining and epistemic network analysis, I have mapped the co-temporal, contemporaneous, and longitudinal dynamics of self-regulation, collaborative roles, and engagement. More recently, this line of research extends to MOOCs and large-scale online contexts, where inferential temporal network methods (TERGM, SIENA) complement descriptive approaches.
Selected Publications
- Temporal Networks in Collaborative Learning: A Case Study (2022)
- The Temporal Dynamics of Online Problem-Based Learning: Why and When Sequence Matters (2023)
- High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning (2020)
- Temporal Network Analysis: Introduction, Methods and Analysis with R (2024)
- Capturing the Temporal Dynamics of Learner Interactions in MOOCs (2025)
- The Relational, Co-temporal, Contemporaneous, and Longitudinal Dynamics of Self-Regulation for Academic Writing (2021)
- Instant or Distant: A Temporal Network Tale of Two Interaction Platforms and Their Influence on Collaboration (2022)