There is a number of evidence that suggests that games and gamified applications can drive collective action in the real world. How individuals interact with problem solving within a gamified environment that can solve problems in the real world is such a hybrid context highly dependent on intrinsic motivation. Here the context of interaction is key to a hybrid approach, blending the characteristics of applied gaming, gamification and gameful design, exploiting the use of game mechanics for encouraging people to engage with tasks that produce real outcomes. The connection between digital interactions and outcomes in the real world forms a contextually hybrid approach, facilitating an experience for a community to collectively contribute to epic objectives and outcomes.
The C4Rs project is very much dependent on smart sensing and collective awareness via its SmartRoadSense application. Collective awareness can be defined as an attribute of communities that helps them solve collective action problems, equivalent to the way that social capital is defined as an attribute of individuals that helps them solve collective action problems (Bourazeri et al., 2016). Collective awareness is a critical aspect within communities which promotes collective action and crowdsourcing through citizen participation that may lead to the accumulation of data and information essential for informing decision making and for solving difficult problems (Pitt et al., 2013).
As part of the “nudging” strategy for encouraging the general public to participate in collecting data for road quality in Europe, we are creating a simple mobile game that is dependent on the data collected through the smart sensing application. We are implementing partial procedural-content generation of game levels based on the collected data, which is a novel approach for reusing and representing data back to the individuals in a more engaging and gameful way.
Come and find out more about this approach at the VS-Games Conference this week. Our very own Mark Lewis will be presenting on our behalf.
The abstract:
Balance Trucks: Using Crowd-Sourced Data to Procedurally-Generate Gameplay within Mobile Games
Mark Russell Lewis, Sylvester Arnab, Luca Morini, Samantha Clarke and Alex Masters, Disruptive Media Learning Lab. (DMLL), Coventry University, Coventry, United Kingdom
Lorenz Klopfenstein, Alessandro Bogliolo and Saverio Delpriori, Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Urbino, Italy
Within the field of procedural content generation (PCG) research, until now, the use of crowd-sensing data has primarily been used as a means for the collection of information and the generation of feedback relating to player experience within games, and game aesthetics [1], [2]. However, crowd-sensing data can offer so much more, supplying a relatively untapped font of information, and inspiration, which might be used within the creation of unique PCG game spaces or content, whilst providing a highly visible outlet for the dissemination of crowd-sensed material to users. This paper examines one such use of crowd-sensed data, the creation of a gamification layer for the CROWD4ROADS (C4RS) [3] application, SmartRoadSense (SRS) [4]. The authors will open with a brief discussion of PCG. Following this, an explanation of the features and aims of the SmartRoadSense application will be provided. Finally, the paper will introduce ‘Balance Trucks’, the SmartRoadSense gamification layer, discussing the concepts behind using crowd-sensed data within its design, its development and use of PCG.
Keywords—gamification; procedural content generation; game design; video game; racing game, crowd-sensing
[1] Togelius, A.J. Champandard, P.L. Lanzi, M. Mateas, A. Paiva, M. Preuss, and K.O. Stanley, “Procedural content generation: Goals, challenges and actionable steps,” Artificial and Computational Intelligence in Games, Dagstuhl Follow-Ups, 6, pp. 61-75, 2013.
[2] Pedersen, J. Togelius, and G.N. Yannakakis, “Modeling player experience for content creation,” IEEE Transactions on Computational Intelligence and AI in Games, 2 (1), pp. 54–67, 2010.
Sylvester Arnab 