Conceptualising a data analytics framework to support targeted teacher professional development

This paper proposes a conceptual framework enabling the development and adoption of descriptive, diagnostic, predictive and recommendatory data analytics in teacher professional learning by harnessing some of the affordances of digital technologies to convert data into actionable insights. The paper argues for a technology-enhanced approach that uses data to support teachers in selecting appropriate professional development (PD) options to improve their professional practice. The ultimate goal is to lay the foundations for a robust and adaptable data analytics framework that could offer tailored PD recommendations based on the developmental trajectories of individual teachers. The paper analyses data-supported personalised professional learning as meaning-making and the appropriation of cultural artefacts within the 'mobile complex' - consisting of structures, agency, and the dynamic interplay between cultural and technological tools and practices. This study undertakes a comprehensive literature review to identify key concepts, gaps, and theoretical insights, informing the development of a data analytics framework. The resultant framework integrates personalisation, teacher agency and autonomy, contextual relevance, and ethical safeguards into PD process, aiming to foster a responsive, collaborative, and context-aware data-supported PD.