Measuring reflective inquiry in professional learning networks: a conceptual framework

Abstract

This study introduces a conceptual framework for measuring reflective inquiry within professional learning networks (PLNs). Reflective inquiry, characterized by collaborative, evidence-based, and dialogic practices, is crucial for fostering professional growth and improving student outcomes. However, current methods for evaluating reflective inquiry often rely on subjective self-reports or labour-intensive discourse analysis, limiting their scalability and objectivity. To address these challenges, this paper explores the potential of AI to identify and assess reflective inquiry in PLN dialogues. The proposed framework evaluates three dimensions: collective dialogue, use of multiple data sources, and depth of reflection. Each dimension is conceptualized along a continuum, allowing for nuanced measurement of interactions ranging from surface-level engagement to critical, evidence-based dialogue. The framework was validated through analysis of 78 hours of PLN sessions, encompassing 2,195 contributions across diverse educational contexts. Results reveal a predominance of surface-level interactions (C1: 64.42%), reliance on informal data (D1: 92.67%), and descriptive reflection (R1: 79.95%). True reflective inquiry, combining high levels of all dimensions (C3, D3, R3), was rare (0.18%), highlighting the need for targeted facilitation. The findings underscore the importance of skilled facilitators in promoting deeper engagement and reflective practices. This framework offers a scalable, objective tool for assessing and enhancing reflective inquiry in PLNs, with implications for professional development and educational improvement. Future research should explore the link between reflective inquiry and changes in teaching practice, as well as strategies to foster deeper reflection among educator