Exploring school factors related to professional learning communities: a machine learning approach using cross-national data

In the ongoing endeavour to increase student learning, restructuring schools into professional learning communities (PLCs) remains a popular strategy globally. Multiple studies have investigated positive outcomes associated with PLCs for students and teachers, but limited knowledge exists about factors associated with well-functioning PLCs, such as leadership, organisation, policies, and student and staff composition. We apply machine learning (ML) to explore relationships between PLCs and a wide range of school factors using the Teaching and Learning International Survey (TALIS) 2018. TALIS 2018 provides unique data for this study since it includes substantial information about how schools are managed and the contexts in which they operate across a wide range of countries. We find support for some of the factors mentioned in the literature, as well as identifying other factors not previously explored. Finally, we discuss the potential for further research on how to create optimal conditions for teachers' engagement in PLCs.