Music teacher biography and its impact on teaching practice

This paper describes research carried out as part of a wider doctoral study on ‘the biography of music teachers, their understanding of musicality and the implications for secondary music education’. Music teachers will come from a range of diverse backgrounds, though research data would suggest that most seem to have been educated as ‘classical’ music performers which will have an affect on what they perceive to be central competencies in the development of young musicians. In turn, this will determine, to some extent, what is taught and learned in the secondary music classroom. This study explores the impact of the biography of secondary music teachers as they seek to develop the musicianship of their pupils and present the activities in which the young people will be expected to participate. A mixed methods approach has been taken, including surveys, observation and interviews. Surveys amongst a sample of experienced and trainee teachers have produced a range of quantitative data on respondents’ experience of and values related to music education; whilst qualitative data in the form of lesson observation notes and transcription of semi-structured interviews have been the result of working with a small sub-set of participants. The outcomes of study have suggested a clear link between biography and classroom practice but that there are also other potential tensions which arise, such as in the subject knowledge development of practitioners as they move from musician to teacher. Implications for a variety of stakeholders in secondary music education include a consideration of the development of subject knowledge together with potential review of national and local education policy, the nature of undergraduate music study and the ‘shape’ of initial teacher training in England.

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.