We present the results of two controlled studies of free-improvised ensemble music-making on touch-screens. In our system, updates to an interface of harmonically-selected pitches are broadcast to every touch-screen in response to either a performer pressing a GUI button, or to interventions from an intelligent agent. In our first study, analysis of survey results and performance data indicated significant effects of the button on performer preference, but of the agent on performance length. In the second follow-up study, a mixed-initiative interface, where the presence of the button was interlaced with agent interventions, was developed to leverage both approaches. Comparison of this mixed-initiative interface with the always-on button-plus-agent condition of the first study demonstrated significant preferences for the former. The different approaches were found to shape the creative interactions that take place. Overall, this research offers evidence that an intelligent agent and a networked GUI both improve aspects of improvised ensemble music-making.

Livecoding is an artistic programming practice in which an artist's low-level interaction can be observed with sufficiently high fidelity to allow for transcription and analysis. This paper presents the first reported "coding" of livecoding videos. From an identified corpus of videos available on the web, we coded performances of two different livecoding artists, recording both the (textual) programming edit events and the musical effect of these edits. Our analysis includes a novel, transition-matrix visualisation of the textual and musical dimensions of this data to create a "performer fingerprint". We show how detailed transcriptions of livecoding videos can be made which, we hope, will provide a foundation for further research into describing and understanding livecoding.

This paper proposes that online HCI studies (such as web-surveys and remotely monitored usability tests) can benefit from statistical data analysis using modern statistical learning methods such as classification and regression trees (CARTs). Applying CARTs to the often large amount of data yielded by online studies can easily provide clarity concerning the most important effects underlying experimental data in situations where myriad possible factors are under consideration. The feedback provided by such an analysis can also provide valuable reflection on the experimental methodology. We discuss these matters with reference to a study of 1300 participants in a structured experiment concerned with head-interaction techniques for first-person-shooter games.

Broadband communication technologies offer households many opportunities, including greater access to education, health and government services, entertainment and social connectivity. Australia appears, however, to be lagging behind other nations in adopting broadband.This paper reports on a study of the relative importance of factors affecting broadband uptake and usage by Australian households. A large data set with 23,093 records was analysed using an innovative statistical technique known as classification tree analysis.Important adoption factors, in approximate order of importance, were: Frequency of Internet usage (indicating needs and benefits of use); Location, a factor likely to indicate, in part, the availability of services; Technophilia, a tendency to acquire technologies in general in the household; and Subscription to Pay TV. User needs and benefits, rather than cost, were found to be primary drivers.