Evidence-based Visualization for INterpretation within Complex information Environments (EVINCE)

Rigor and precision in understanding training design exists within the academic literature of the learning sciences but accessing the state-of-the-art in the learning sciences and applying its lessons to specific requirements questions is a daunting challenge. A requirements analyst may not know where to look or how to interpret and apply the information they find in the academic literature. Training designs that are more effective, faster to learn, and/or easier to retain are sometimes overlooked. To address this problem, Soar Technology, Inc. and the Georgia Research Institute propose EVINCE, to support evidence-based visualization and interpretation within complex information environments. Building on prior work in semantic search, adaptive training, visualization, and ontologies, the EVINCE architecture supports general workflow and get of intelligent systems capabilities for finding and apply published results. An implementation of this architecture, EVINCE-TDA (Training Design Alternatives) provides allows users to identify relevant learning sciences research, helps them to interpret and to orient it to their specific training decision problems, and then to apply these evidence-based guidelines to those decision problems.