Personalising game difficulty to keep children motivated to play with a social robot: a Bayesian approachSchadenberg, B., Neerincx, M. A., Cnossen, F. & Looije, R., Jun-2017, In : Cognitive Systems Research. p. 222-231
Research output: Contribution to journal › Article › Academic › peer-review
Playing games with a social robot should be engaging and keep a child motivated to play the games with the robot for a longer period of time. One aspect that can affect the motivation of a child is the difficulty of the game. A game should be perceived as challenging, while at the same time, the child should be confident to meet the challenge. We designed a user modelling system that adapts the difficulty of a game to the child's skill, in order to provide children with the optimal challenge. To this end, we used a Bayesian rating system to estimate the child's skill and performance. In the experiment, we used our user modelling system to test whether children whom are optimally challenged are more intrinsically motivated to play games with the robot, than children whom are not optimally challenged. 22 children participated in the experiment, aged between 10 and 12 years old. While we were not able to provide the optimal challenge, this study shows that using a Bayesian rating system to measure the skill and performance of children in playing a game is feasible, even without accurate estimates of the difficulty of items and skill of the children. We out- line multiple ways in which a rating system can be used to improve the child-robot interaction, other than adapting the difficulty of games, illustrating the possible benefits of using a rating system on a social robot.
|Journal||Cognitive Systems Research|
|Publication status||Published - Jun-2017|
- Social Robotics, User Modeling, Rating System, Child-Robot Interaction