We propose to develop computational models and tools for rapid training and development of collective expertise. We will enhance the currently available online courses offered by NovoEd to develop approaches that automatically optimize the learning ecosystem by considering parameters of content, peer interaction and collaboration, as well as the instructor and mentors to maximize group performance. Phase I of the project includes computational modeling and design of experiments for studying and optimizing group formation and dynamics in online courses, as well as using peer feedback for increasing incentives for collaboration.
Benefit: Effective solutions for connected, collaborative, and engaged learning and development of collective expertise can be invaluable for the military, government and the private sector. In particular, for the active personnel deployed around the world, the internet may be the only way to access educational material at most times. For veterans, online platforms give us the opportunity to offer education to many who are either not prepared, or do not have the time or financial means to go to a university classroom. The potential of this novel form of online education to increase the reach and reduce the cost of education of groups who desire to rapidly and effectively obtain collective expertise in complex areas will also have a positive impact on the private sector, as evidenced by our current penetration into that market with our already successful, but less sophisticated and individual learner oriented product.
Keywords: Rapid training, Rapid training, assessment and evaluation, Decision-making, collective expertise, ADAPTIVE LEARNING
