Temporal and Sequence Methods
Most of what we know about learning comes from cross-sectional snapshots, yet the questions that matter most — how engagement evolves, whether strategies transfer across courses, why some students recover from setbacks while others do not — require longitudinal and temporal methods. My research tackles these questions using very large datasets that span entire degree programs, revealing patterns invisible to single-course studies.
Through sequence analysis, I have mapped the longitudinal trajectories of online learning strategies across 10 successive courses, identifying stable “intense” trajectories associated with deep learning alongside fluctuating and surface-learning pathways. Using multi-channel sequence analysis, I have shown how engagement and achievement co-evolve across multiple dimensions simultaneously. Hidden Markov models and mixture models have allowed me to uncover the heterogeneous transition dynamics of engagement states and to identify the instructional variables that trigger transitions between engaged and disengaged states. The VaSSTra method (Variables to States to Trajectories), which I co-developed, provides a general-purpose framework for converting any set of longitudinal measurements into states, sequences, and clustered trajectories — applicable at any time scale from days to years.
Selected Publications
- Intense, Turbulent, or Wallowing in the Mire: A Longitudinal Study of Cross-Course Online Tactics, Strategies, and Trajectories (2023)
- Multi-Channel Sequence Analysis in Educational Research: An Introduction and Tutorial with R (2024)
- Sequence Analysis in Education: Principles, Technique, and Tutorial with R (2024)
- The Why, the How and the When of Educational Process Mining in R (2024)
- From Variables to States to Trajectories (VaSSTra): A Method for Modelling the Longitudinal Dynamics of Learning and Behaviour (2023)
- Capturing Temporal Pathways of Collaborative Roles: A Multilayered Analytical Approach (2025)
- The Temporal Dynamics of Procrastination and its Impact on Academic Performance (2024)