Abstract
ABSTRACT 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.