My research focuses on developing rigorous ways to quantify, model, and support learning processes through learning technologies. I use mixed-methods approaches to study learning as a complex, dynamic, and context-dependent process, combining behavioural trace data, self-report measures, performance data, and qualitative evidence. A central goal of my work is to develop learner models that capture how learners plan, monitor, and adapt their learning over time and across different tasks and learning environments.

My current work focuses on the design and empirical evaluation of adaptive and personalized learning technologies grounded in the science of learning. I am developing a framework for educational AI systems that adapt to learners’ behaviours, individual differences, and evolving learning pathways. The aim is to help learners develop more effective learning tactics and strategies.

My interdisciplinary background brings together learning science, AI, and data science. I hold a PhD in Learning Analytics and master’s and bachelor’s degrees in Computer Science.

Courses I have taught in the past or currently teach include:

• LIBR 559C Python Programming
• LIBR 554/ARST 554 Database Design
• LIBR 552 Human-Centered AI

• Learning Analytics
• AI in Education
• Human-Centred Design