Crowdsourcing Education: A Game-Theoretic Analysis of Contribution and Learning in Peer to Peer Education Platforms

The Internet has provided a unique opportunity for many alternatives to the traditional classroom education, and one emerging example is the advent of online peer to peer or crowdsourced education platforms. On such websites any user is free to contribute courses in his or her area of expertise, and the platform aggregates these crowd submissions. In this paper we model the contribution and learning behavior in such a system, with the goal of determining the effect content presentation can have on contribution incentives, the distribution of contributed material with respect to difficulty level, and ultimately learning outcomes. Our primary contribution in accomplishing this goal is the construction of a rich game-theoretic model capturing the learning and contribution mechanics. The model includes two populations, learners and potential contributors, in which each agent from either population has a discrete skill level. The system evolves over a series of discrete time steps which alternate between rounds of learning and contribution. We represent contribution in each time step as a complete information simultaneous game under a free-entry Nash Equilibrium, such that contributors may elect whether to participate and conditional upon participating create a course and an associated fee. Potential contributors are strategic in an impatient sense in that they seek to maximize profit in the next round of learning. To model the effect of content organization we introduce the notion of a mechanism as the platform’s method of matching a learner to a course, which the learner can enroll in or decline. As a baseline mechanism and simplification of unorganized content we consider random course delivery. We show this mechanism can lead to inefficiencies, including many redundant courses targeted at novices and a lack of advanced content. Next we introduce an alternative mechanism which breaks down the