Case Study Educational Prediction Markets: Construction Project Management Case Study Ivan Damnjanovic, M.ASCE 1 ; Vahid Faghihi 2 ; Chyllis Scott 3 ; Erin McTigue 4 ; and Kenneth Reinschmidt 5 Abstract: Effective teaching of engineering concepts relies both on carefully designed lesson plans that meet specific learning outcomes and on classroom activities that students find engaging. Without student engagement, even the best designed plans will fail to meet their out- comes. In other words, students need to be actively involved in the learning process. The objective of this paper is to present a case study of applying a novel active learning method, specifically educational prediction markets (EPM), for teaching project management classes at a major research university. This method was investigated for its effectiveness in engaging students and promoting learning of probabilistic reasoning without explicit teaching. Student surveys, following the EPM implementation, revealed both advantages and disadvantages. The two key benefits reported by the students were: (1) providing better connections between the materials taught in the class and realities of construction projects; and (2) increasing overall interest and enthusiasm in learning about project risk management as a result of the gamelike nature of the process. The primary disadvantage was disengagement by a subset of students because of perceptions that fellow students were manipulating the market results. DOI: 10.1061/(ASCE)EI.1943-5541.0000127. © 2013 American Society of Civil Engineers. CE Database subject headings: Construction management; Case studies; Project management; Predictions; Economic factors. Author keywords: Prediction market; Active learning; Construction; Case study. Introduction Teaching how to identify, assess, and manage project risks presents many challenges. One of the greatest challenges instructors face is the inherent difficulty of linking probabilistic predictions with actual observations. For example, it is possible to predict that the probability of rain tomorrow is 80%; but then, it may not rain. Was the prediction good? It is difficult to answer this question because predictions are only probabilistic propositions rather than deterministic observations. This conundrum of teaching probability concepts is particularly visible when trying to predict outcomes such as completion time of construction projects. Although some engineering disciplines can use laboratory experiments to validate the uncertainty in prediction, this approach cannot be applied to construction management. For example, 100 laboratory tests can be run to determine the proba- bility that a concrete sample taken from Batch A will withstand the stresses required by seismic codes. This empirical approach to de- termining risk is visible and obvious, even for novices to the field such as undergraduate students. However, for complex projects in which one cannot directly observe outcomes and estimate proba- bilities, how does one assess a probabilistic prediction? In teaching project risks, instructors often rely on numerical methods, such as Monte Carlo simulations (MCS). Although sim- ulations may be more transparent than other methods, they can be considered susceptible to manipulation by the simulator. Teaching experience shows that students often view the MCS approach as too abstract, which in turn can make students suspicious and disen- gaged from further exploration. This feedback loop—in which a lack of realism diminishes student engagement, and deficiency in engagement prevents further investigation to understand abstract concepts—represents a major hurdle in teaching engineering project risk management. Until teachers gain students’ attention and engagement by grounding the learning within real world ex- amples, the learning process is stalled. The objective of this case study is to document implementation of educational prediction markets (EPM) in undergraduate project management classes. For background, prediction markets have re- cently found applications in many fields, including education, project management, and scientific research (Hanson 1999; Arrow 2008). In a prediction market, a participant buys or sells shares in the realization of a specific well-defined outcome. If the predicted outcome occurs, the participant can exchange the shares for a “reward” of 100 units per share. If the predicted outcome does not occur, the value of the shares becomes 0. If a particular outcome is likely, the price of shares will go up (as demand grows) and vice versa; as the specified outcome seems less likely to happen, the market price will go down. Hence, prediction markets represent the social trade forums that run for the primary purpose of aggregating information in an effort to forecast future events (Tziralis and Tatsiopoulos 2007; J. E. Berg, F. Nelson, and J. A. Rietz, Working Paper, Tippie College of Business, University of Iowa, 2003). Arguably, the most important issue with implemen- tation of a market is its performance as a predictive tool (Wolfers and Zitzewitz 2004). On a practical note, in EPM, these prices and 1 Associate Professor, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843-3136 (corresponding author). E-mail: idamnjanovic@civil.tamu.edu 2 Ph.D. Candidate, Texas A&M Univ., Zachry Dept. of Civil Engineering, College Station, TX 77843-3136. E-mail: savafa@tamu.edu 3 Ph.D. Candidate, Texas A&M Univ., Dept. of Teaching Learning and Culture, College Station, TX 77843. E-mail: chyllisscott@tamu.edu 4 Assistant Professor, Texas A&M Univ., Dept. of Teaching Learning and Culture, College Station, TX 77843. E-mail: emtigue@tamu.edu 5 J. L. Frank/Marathon Ashland Petroleum LLC Chair in Engineering Project Management, Zachry Dept. of Civil Engineering, College Station, TX 77843-3136. E-mail: kreinschmidt@civil.tamu.edu Note. This manuscript was submitted on June 21, 2011; approved on May 22, 2012; published online on May 24, 2012. Discussion period open until September 1, 2013; separate discussions must be submitted for individual papers. This paper is part of the Journal of Professional Issues in Engineering Education & Practice, Vol. 139, No. 2, April 1, 2013. © ASCE, ISSN 1052-3928/2013/2-134-138/$25.00. 134 / JOURNAL OF PROFESSIONAL ISSUES IN ENGINEERING EDUCATION & PRACTICE © ASCE / APRIL 2013