Using Online Learner Trace Data to Understand the Cohesion of Teams in Higher Education

I lead a research program at the intersection of learning analytics and artificial intelligence in education. Collaboration is among the most valued capabilities in modern curricula, yet it remains largely invisible to assessment. Beginning with my doctoral work on team cohesion in technology-mediated learning, I have progressively built the methods and the tools to make collaborative problem-solving observable in authentic settings — capturing collaborative skills and learning dispositions in real time from classroom interaction, and returning evidence-informed feedback to teachers. The aim is to move the field beyond post hoc evaluation of teamwork toward responsive, in-situ support for collaboration.

I work with mixed methods and a commitment to equity by design, developing AI that mitigates bias and cultivates an inclusive, AI-competent workforce. A complementary strand of my research examines human-AI teaming: how trust forms in human-autonomous teams, and what an AI requires to be a credible analytic partner in learning.

Both strands converge on a single ambition — assessment of collaboration that teachers can trust and act on.