Network Analysis in Education

Networks are a powerful lens for understanding learning as a relational phenomenon. Rather than treating students as isolated entities, network analysis reveals how learners connect, exchange information, and build social capital through their interactions. My work in this area spans collaborative learning networks in medical education, problem-based learning, language learning, and computer science education — consistently demonstrating that a student’s position in the network carries meaningful signals about their engagement, knowledge construction, and academic outcomes.

A central thread of this research concerns methodological rigor: which centrality measures reliably capture students’ participation and social dimensions, which network configurations yield reproducible results, and how diffusion-based centralities can capture information exchange processes at scale. Through a series of large-scale empirical studies and meta-analyses, I have established that network parameters can serve as early-warning indicators of academic performance. Other threads include modeling network robustness and rich-club phenomena, studying how social capital and long-term ties influence learning, and developing guidelines for best practices in educational network analysis.

Selected Publications

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