NOVA: An Interactive Graphics-Scripting Platform for Education and Computational Research

Google Tech Talk May 10, 2013 (more info below) Presented by Wayne M. Getz, Richard M. Salter, Nick Sippl-Swezey ABSTRACT Current graphically-driven coding platforms for building computational models are either confined to a restricted selection of in-house elements or have a flat structure that facilitates the formulation of either dynamical systems models or individual-based, event-oriented (agent) models, but not both. NOVA is a hierarchically structured, modularized platform with a graphics-based IDE. Nova models are constructed using graphical components that are subsequently captured and converted to a JavaScript extension called NovaScript that seamlessly merges dynamic-system and agent based modeling paradigms. It is simple enough to be used in the K-12 classroom to meet the Next Generation Science Standards in computational thinking, as described in A Framework for K-12 Science Education. It is also powerful enough to build full scale computational models for research in the environmental and human sciences fields. Nova features include: • Modularity and open-ended scalability with hierarchical levels that can be independently run, tested,evaluated and intertwined. Such levels may be reused in multiple models. • Wrapping of any code required for model implementation into Nova code chips, which are portable and reusable components of the IDE. • Platform extension through the use of plugins. • Proliferation and movement of agents over real landscapes • Interfacing the platform with other platforms to take advantage of existing tool boxes (e.g. geographic information systems, R statistical packages). Since models can be depicted as a graphical set of wiring diagrams at each hierarchical level of the formulation, recyclable modules and code chips can be easily snapped in and out of the model, allowing the platform to facilitate the development of communities of applied computational scientists that have rudimentary coding skills, and enabling them to communicate the structure of very complex models, build upon common experiences, assemble very large models rapidly and accurately with considerable confidence in their voracity to address the most challenging societal problems of today: poverty, disease, global change.