Identifying and Developing Mathematical and Creative Genius
October 17, 2016 - March 31, 2017
Core Funding Area:
Genetics and Genius
Director: Prof. Po-Shen LohInstitution: Expii, Inc.
Expii has a new model for promoting the identification and development of extraordinary math and science talent, and for expanding the base of people with extraordinary talent. This approach is two-pronged: 1. Boosting interest through challenges 2. Providing a free, adaptive, and personalized technology tool that can lead any identified/interested individual to the highest levels Both of these initiatives have gained momentum and reach people everywhere in the world via the internet. This proposal supports this new model, which is freely released to the public on expii.com. The first prong focuses on developing and popularizing weekly sets of creative math puzzles. Its style is motivated by the Discovery Channel, and Expii’s puzzles are carefully designed to combine intense challenge with shareability among the mainstream public. The intiative this by releasing five puzzles per week that ramp up in difficulty, and by authoring very non-standard problems. This powers a funnel for drawing traffic to other projects that further develop talent, including the Spirit of Ramanujan Talent Initiative (http://expii.com/ramanujan). The second prong is Expii's long-term R&D project. Expii’s approach uses the Elo Rating System to measure talent at high resolution at every point of the spectrum. Its viability at the extreme ends is a distinctive feature of the Elo method, and is in fact the reason this method has been used to great success in ranking World Champion chess players. Expii uses a unique mathematical perspective to decompose the general problem of personalized learning into a series of tractable math/algorithm problems, and packages the entire experience into an interaction style more similar to a maps app’s automatic navigation system than to a traditional classroom. During the period of this support, Expii will develop and calibrate their new infrastructure, and populate it with a sufficiently large body of openly licensed problems and lessons to operate on.
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