Exploring How Video-informed Feedback Supports Embedded Educator Professional Learning Between Middle Leader Facilitators and Teachers
MetadataShow full item record
The purpose of this study was to explore how video-informed feedback supported embedded educator professional learning (EPL) between middle leader (ML) facilitators and teachers, addressing gaps in EPL literature pertaining to the role of feedback and video in embedded, ML-facilitated EPL. Two research questions guided my study: How does a video-informed feedback model support embedded EPL between ML facilitators and teachers? and How do video-informed feedback dialogues between ML facilitators and teachers evolve to support EPL over time? An embedded multiple case study design was used to study video-informed feedback between ML facilitators and teachers over one academic year in the context of a regional, math-based EPL initiative. A total of seven educators participated in this study: two ML facilitators and five teachers from two rural district school boards. Each facilitator represented a case; embedded units consisted of facilitator-teacher pairs. Primary data sources included semi-structured individual and dyadic interviews with participants and audio recordings of ML facilitator-teacher pairs’ feedback dialogues. Data were analyzed using inductive descriptive and deductive provisional coding techniques to develop within case themes. Within case themes were further analyzed to develop four cross-case assertions in response to how video-informed feedback supported embedded EPL between ML facilitators and teachers. Cross-case assertions pertained to interrelated learning goals, video-informed feedback dialogues, responsive collaboration, and video-informed praise. Key contributions of this research include my conceptual feedback model of embedded, ML-facilitated EPL as well as the importance of: (a) more knowledgeable-other support of both ML facilitators’ and teachers’ learning and practices—especially their data literacy; (b) setting and working toward interrelated goals for teachers and students to enhance EPL outcomes; (c) explicitly leveraging multi-source feedback models that incorporate diverse classroom data, including video, to help ML facilitators and teachers co-construct practice-based evidence to inform EPL; and (d) video-informed praise from ML facilitators to teachers which enables ML facilitators’ subsequent coaching feedback and teachers’ self-regulation. More broadly, this study highlights the importance of trusting professional relationships coupled with a focus on diverse sources of practice-based evidence to enable meaningful, constructive dialogues in the context of embedded, ML-facilitated EPL and to support desired outcomes for educators and students.
URI for this recordhttp://hdl.handle.net/1974/24483
Request an alternative formatIf you require this document in an alternate, accessible format, please contact the Queen's Adaptive Technology Centre
The following license files are associated with this item: