Simbrain: A visual framework for neural network analysis and education

Simbrain is a visually-oriented framework for building and analyzing neural networks. It emphasizes the analysis of networks which control agents embedded in virtual environments, and visualization of the structures which occur in the high dimensional state spaces of these networks. The program was originally intended to facilitate analysis of representational processes in embodied agents, however it is also well suited to teaching neural networks concepts to a broader audience than is traditional for neural networks courses. Simbrain was used to teach a course at a new university, UC Merced, in its inaugural year. Experiences from the course and sample lessons are provided.

Towards a more effective model for distance education

What is the most effective model for academic distance education, given that drop-out numbers in traditional distance education institutions are too high and the demands from the various stakeholders are changing? In this paper this question is answered from the perspective of the Open University of the Netherlands (OUNL). The OUNL has planned to redesign its educational model from the traditional guided self-study model towards a model of active online learning. In essence this means that education will be less content driven; more focus is put on activating students to engage with real world problems supported by tutors and peers using distance media. The drivers for change, the change process and the resulting redesign of the educational model are presented in this paper.