Rate My Professor Zachari Swiecki

Swiecki's research specializations lie in learning analytics, with a particular emphasis on collaborative learning settings. He has played a central role in the development and dissemination of Quantitative Ethnography, an emerging methodology that integrates qualitative analysis, statistics, and data science into a unified framework. His current work includes modeling collaborative processes in domains such as engineering design, medicine, and the military, as well as designing real-time systems for monitoring teams. Recent projects focus on understanding how students work with generative artificial intelligence in writing tasks. Key publications include 'Community of inquiry in motion: Modeling inquiry dynamics with movement analysis (MOVA)' (2026, Computers and Education), 'Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support' (2026, Computers and Education), 'Analytics of self-regulated learning strategies and scaffolding: Associations with learning performance' (2025, Computers and Education: Artificial Intelligence), 'Asking generative artificial intelligence the right questions improves writing performance' (2025, Computers and Education: Artificial Intelligence), and 'Assessment after Artificial Intelligence: the research we should be doing' (2025, Journal of University Teaching and Learning Practice). With 77 research outputs documented on the Monash research repository, Swiecki contributes significantly to advancing quantitative methods in educational psychology and learning sciences. He was a finalist for the Naomi Miyake Best Student Paper Award at the 13th International Conference on Computer-Supported Collaborative Learning (CSCL 2019) for 'Does order matter? Investigating sequential and cotemporal models of collaboration'.