Collaborative Teacher Educator Professional Development in Europe: Different Voices, One Goal

In this paper we present an embedded case study focussed on the learning activities provided for and by us through our involvement in an international forum focused on the professional development of teacher educators. The aim of this research was to get more insights into the complicated processes of professional learning across national borders. Data included personal narratives about learning and documentary analysis of written accounts of the forums’ activities. Following a collaborative self-study approach we utilised an interactive exploration of the data, using coding techniques derived from grounded theory. We conclude that our professional learning can be seen through two inter-related perspectives. The first perspective is the interplay between our own learning and the ways in which we want to support colleagues in their professional development. The second perspective is the reciprocal effect of working in national as well as in transnational contexts. By studying our professional learning processes we developed insights in how a shared communal international forum can be established without losing individual voices and national perspectives. Moreover, by our involvement in an international forum we also continue to develop our own self-understanding as ‘educators of teacher educators’.

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.