Using Prediction Markets to Deliver Authentic Learning Experiences

Using Prediction Markets to Deliver Authentic Learning Experiences

Patrick Buckley, John Garvey, Fergal McGrath
Copyright: © 2012 |Pages: 16
DOI: 10.4018/978-1-61350-074-3.ch004
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Abstract

Mass higher education presents serious problems to implementing active learning. Large class sizes means that traditional active learning strategies are becoming more and more difficult to implement, due to the administrative burdens associated with them. In this chapter, the authors present prediction markets as a pedagogical tool which can be used to allow teachers to implement active learning in a large group teaching environment without imposing prohibitive administrative overheads. They outline the benefits for students in the cognitive and affective domains of learning. They move on to present a case study describing in detail how our methodology can be implemented and conclude by presenting research on the effectiveness of our approach in the cognitive domain of learning. The authors conclude that prediction markets are a powerful tool for implementing active learning in a large group teaching environment.
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Background

Active Learning

Constructivist learning theories are based upon the concept that knowledge is constructed by an individual using their own cognitive processes. Learning is dependent on both feedback from the environment and social interactions (Dunlap & Grabinger, 1996). Constructivism provides the theoretical justification for active learning. Active learning promotes critical thinking and teaches learners to search for, analyze and apply knowledge to solve the complex problems which are a hallmark of the modern world. According to Bostock (1998) active learning is defined by five specific attributes: Realistic Learning Experience; Student Led Learning; Generative Learning; Realistic Assessment Mechanisms; and Collaborative Learning.

Active learning requires that the learning experience should be realistic and should accurately reflect both the situation where the learning is expected to be applied and the decisions that will be required in that situation. In general, the learning experience should be built around real-world problems, events and issues (Bostock, 1998).

A second attribute of active learning is that it should promote initiative and responsibility in the student learning process (Kinchin, Chadha, & Kokotailo, 2008). Active learning ideally involves generative learning in the form of the creation of a knowledge artefact. This may be a physical artefact, such an essay, or an intellectual artefact. This implies that learners should actively engage in understanding course content by creating artefacts related to the course (Zantow, Knowlton, & Sharp, 2005).

Active learning requires that assessment mechanisms should prompt learners to use skills rather than describe them. Assessments should have a reasonable level of complexity and be exemplars of how skills would be applied in a realistic setting. A final criterion identifies the positive benefits of peer group collaboration. Most notably, it can overcome the feeling of isolation which can occur in the learning experience, particularly in large groups (Hogan & Kwiatkowski, 1998).

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