Complex Systems

Learning is not a linear process — it is a complex dynamic system where cognitive, motivational, and behavioral components interact, feed back on one another, and give rise to emergent patterns that cannot be predicted from any single component alone. My research applies the complex-systems paradigm to model the person-specific mechanisms that drive self-regulated learning, engagement, and academic trajectories. The central insight is that the “average student” is a statistical fiction: individual learners differ not just in level but in the very structure of how their learning processes unfold.

Using psychological networks and Gaussian Graphical Models, I have demonstrated that self-regulation is a complex, dynamic system in which sub-processes influence each other in ways that vary across individuals and across time scales. Trait-level dynamics differ from state-level dynamics, and interventions derived from group averages are unlikely to be effective for any particular student. This work establishes the foundation for idiographic analytics — person-specific models built from individual data that resolve the privacy and ethical constraints of traditional approaches while delivering precise, actionable insights. The implications extend beyond education to well-being and mental health, wherever understanding within-person dynamics matters.

Selected Publications

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