TY - JOUR
T1 - Exploring school factors related to professional learning communities
T2 - A machine learning approach using cross-national data
AU - Christensen, Anders Astrup
AU - Nielbo, Kristoffer Laigaard
AU - Gümüs, Sedat
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Exploratory analysis
KW - International Large Scale Assessment (ILSA)
KW - Machine learning
KW - Professional learning communities (PLCs)
KW - School leadership
KW - Professional Learning Communities (PLC)
KW - exploratory analysis
KW - Machine Learning (ML)
KW - International Large Scale Assessment
KW - school leadership
UR - https://www.scopus.com/pages/publications/85197299437
U2 - 10.1080/03055698.2024.2369855
DO - 10.1080/03055698.2024.2369855
M3 - Journal article
SN - 1465-3400
VL - 51
SP - 805
EP - 825
JO - Educational Studies
JF - Educational Studies
IS - 5
ER -