We are building a game-like social platform called DOCET that helps users showcase, rate and get validated on all forms of learning. The problem we wish to address is how to earn credit for gaining new knowledge independent of how and from where that knowledge is gained. Assumptions: 1) There is no lack of great content on any given topic available today. 2) People consume vast amount of new information from a varied array of sources. So how can one earn value for all new information consumed and translate that into a knowledge asset that individuals can capitalise? Docet solves the problem by incorporating a level based validation system, similar to playing your favourite game, and mapping incremental progress in learning in all the areas of one’s interest. So matter how much or how little the gains in knowledge is, the user gains value for it with total autonomy in choice of source and topic of content. The user uploads supporting documentation, in a format of her choice, which showcases her progress in learning. The users more knowledgeable (i.e. users at higher levels) rate those below them on proofs of learning submitted and upon receiving a certain threshold value based on collective ratings from multiple high level users, she progresses to the next level, or a multiple level jump if she has earned multiple level progress points. The relative standing of the user in a particular category is always fluid i.e. it changes with the behaviour of everyone within the same category. This means that you can identify valuable skills to within your cohort that is more valuable to others and thus can choose to concentrate on developing it more quickly.