University of Oulu

An explorative study of applying Prediction Markets in course evaluation

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Author: Wang, Kang
Organizations: 1University of Oulu, Faculty of Science, Department of Information Processing Science, Information Processing Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/URN:NBN:fi:oulu-201305301384
Language: English
Published: Oulu : K. Wang, 2013
Publish Date: 2013-05-31
Physical Description: 81 p.
Thesis type: Master's thesis
Tutor: Markkula, Jouni
Ahmad, Muhammad
Reviewer: Markkula, Jouni
Halonen, Raija
Description:
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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