Knowledge Stock Exchanges: a Co-Opetitive Crowdsourcing Mechanism for E-Learning

Modern information and communication technologies (ICT) provide numerous opportunities to support e-learning in higher education. Recent devlopments such as Massive Open Online Courses (MOOCs) utilize the scalabiltiy and interactivity of the ICT to broaden the accessibility of university education. However, the potential of ICT in enhancing students’ learning experience and success is far from being fully utilized. One potential area for the development of new e-learning mechanisms is at the intersection of collective intellegence and crowdsourcing mechanisms: The knowledge-disseminating ability of a collective intelligence platform combined with the interactivity and participative nature of crowdsourcing knowledge from fellow students may enhance motiviation and acceptance of students’ learning. Following a crowd-based approach we present a prototype that offers a highly collaborative and competitive learning environment to improve the mutual exchange of knowledge as well as to encourage the development of a knowledge community. Our approach draws upon the principle of virtual stock markets (also “prediction markets”), a well-known collective intelligence mechanism which we enhanced with crowdsourcing elements. We describe the proposed system architecture, evaluate the practical feasibility of our prototype in the field and provide implications for future research.