An explorative study of applying Prediction Markets in course evaluation
1University of Oulu, Faculty of Science, Department of Information Processing Science, Information Processing Science
|Online Access:||PDF Full Text (PDF, )|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201305301384
|Publish Date:|| 2013-05-31
|Thesis type:||Master's thesis
Prediction Markets is a new emerging efficient method for predicting the likelihood of uncertain future events, and also it has been used successfully in various fields. Compare to traditional forecasting methods, it can provide more accurate results and reflect continuous real-time information. In this research, we focus on its application in course evaluation. This research includes a Prediction Markets-based systematic literature review, in which there are 120 articles included. They are analysed based on four main categories: description, theoretical work, organization management, and applications. The results demonstrate that few of research take Prediction Markets into consideration as an evaluation system and it has not been used for course evaluation purpose. Course evaluation in universities is quite meaningful. Traditional course evaluation methods have been proved to be low response rate, time consuming, inaccurate data and lacking interactions between teachers and students. In this research, the Prediction Markets-based course evaluation system is designed as a new solution of evaluating the course quality. We present an explorative experiment which is conducted in a real course environment with 49 students involved. The students are divided into two groups: Prediction Markets-based group and Traditional group, with the purpose of comparing the difference between Prediction Markets-based course evaluation system and traditional in use course evaluation system. In conclusion, the study confirms the feasibility of extending Prediction Markets in course evaluation use. Additionally, a possible solution of designing and implementing Prediction Markets-based course evaluation system is presented. Further studies should focus on the issues of optimizing the Prediction Markets-based course evaluation system.
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