This chapter examines the potential of the application of an individual creativity-enhancing technique (called SoloBrainstorming, or SBS) to improve the level of creativity of Information Technology (IT) students in performing information system (IS) requirements determination. Requirements determination, in the context of software development, involves gaining an understanding of the underlying issues related to a business problem, and also considering potential solutions. The chapter begins with a definition of creativity, followed by an overview of strategies suggested to enhance creativity. The SBS technique is then introduced, followed by a report of empirical results from its application. Finally, we offer advice for IT education in terms of incorporating creativity-enhancing techniques into the IT course curriculum.

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pžzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.