Learning analytics in educational organisations: Models as boundary objects between communities of practice
The widespread availability of learner-related data has the potential to empower students, teachers, parents and school leaders by providing critical insights into the learning process. However, fostering a widespread organizational culture of data-informed learning and teaching practice remains a significant challenge. This is in part due to the need for multidisciplinary experts collaborating with practitioners to develop processes that can readily facilitate the translation of data into pedagogical action. The presentations focuses on the question of how to accelerate and deepen the uptake of new data-supported practices in schools and universities. I will introduce a theory of integrated individual and organizational learning that suggests that practices of data-intensive decision making can be expanded in educational organisations by engaging these in joint data practices with experts from two fields where innovating data practices are continuously developed: Educational Data Mining including Learning Analytics and the Learning Sciences. The approach focuses on knowledge objects, in particular in their role as boundary objects, that is in their function to facilitate work across different kinds of boundaries, in particular disciplinary boundaries and organizational boundaries [4, 5]. The creative frictions that generate from discussions surrounding these boundary objects can facilitate the broader adoption and dissemination of innovations within and across schools. I will discuss the suitability of number of formats for knowledge objects to serve as boundary objects, including formats that are grounded in methods of meta-modelling.
Lecture at NEMO2016
Date/Time: Tuesday, July 26, 2016 at 10:00