Learning from patterns during information technology configuration.

This paper asks how people can be assisted in learning from practice, as a basis for informing future action, when configuring information technology (IT) in organizations. It discusses the use of Alexanderian Patterns as a means of aiding such learning. Three patterns are presented that have been derived from a longitudinal empirical study that has focused on practices surrounding IT configuration. The paper goes on to argue that Alexanderian Patterns offer a valuable means of learning from past experience. It is argued that learning from experience is an important dimension of deciding “what needs to be done” in configuring IT with organizational context. The three patterns outlined are described in some detail, and the implications of each discussed. Although it is argued that patterns, per se, provide a valuable tool for learning from experience, some potential dangers in seeking to codify experience with a patterns approach are also discussed.

Sonifying the solar system.

Sound is potentially an effective way of analysing data and it is possible to simultaneously interpret layers of sounds and identify changes. Multiple attempts to use sound with scientific data have been made, with varying levels of success. On many occasions this was done without including the end user during the development. In this study a sonified model of the 8 planets of our solar system was built and tested using an end user approach. The sonification was created for the Esplora Planetarium, which is currently being constructed in Malta. The data requirements were gathered from a member of the planetarium staff, and 12 end users, as well as the planetarium representative tested the sonification. The results suggest that listeners were able to discern various planetary characteristics without requiring any additional information. Three out of eight sound design parameters did not represent characteristics successfully. These issues have been identified and further development will be conducted in order to improve the model.