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Bob Schadenberg - Modelling the user’s skill and performance with the use of a Bayesian rating system

18 December 2012


Playing educational games with a social robot can provide a user with a way of learning that is encouraging and engaging. For a social robot to be an effective tutor, the user has to be motivated to play the educational games with the robot for a longer period of time. One aspect that can affect the motivation of the user is the difficulty of the game. A game should be challenging, while at the same time the user should be confident to meet the challenge. We designed a user modelling system that adapts the difficulty of a game to the user’s skill, in order to provide users with the optimal challenge. To this end, we used a Bayesian rating system to estimate the user’s skill and performance. In the experiment, we used our user modelling system to test if users who are optimally challenged are more intrinsically motivated to play games with the robot, than users that are not optimally challenged. Furthermore, we evaluated whether the Bayesian rating system could be used to detect a loss of motivation to play the current game with the robot, by relating the expected performance to the actual performance. 22 children participated in the experiment, aged between 10 and 12 years old. Due to not having enough data, we were not able to achieve the measurement precision that is required to make reliable estimations of the probability of a participant answering an item correctly. Because the participants were not optimally challenged, we cannot answer whether the participants were more intrinsically motivated to play the games. Also, there were not enough events where there was a large discrepancy between the expected performance and the actual performance to conclude if and how reliable the detection of a loss of motivation to play the current game with the robot was. We discuss several improvements that can be made to the user modelling system.

Last modified:31 May 2018 4.05 p.m.

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