a1 University of Michigan, Ann Arbor
a2 University of Michigan, Ann Arbor
Prediction markets are designed to aggregate the information of many individuals to forecast future events. These markets provide participants with an incentive to seek information and a forum for interaction, making markets a promising tool to motivate student learning. We carried out a quasi-experiment in an introductory political science class to study the effect of prediction markets on student engagement with the course topics. Although we found no significant improvement in students' enthusiasm or extent of topical reading, we did find that those students who were already reading broadly at the course start were more likely to trade actively in the markets. These findings indicate that prediction markets may be most successful as an education tool in settings, like graduate education, where individuals are already knowledgeable about the topics of the market, instead of an introductory learning context.
Cali Mortenson Ellis is a PhD candidate in public policy and political science at the University of Michigan, Ann Arbor. She can be reached at firstname.lastname@example.org.
Rahul Sami is an associate professor in the School of Information, University of Michigan, Ann Arbor. He can be reached at email@example.com.
A version of this work was presented at the 9th International Conference on Computer Supported Collaborative Learning (CSCL): Connecting Computer Supported Collaborative Learning to Policy and Practice, July 4–8, 2011, at The University of Hong Kong.