Mindless repetition: Learning from computer games

Coyne, Richard

doi:10.1016/s0142-694x(02)00052-2

Cited by 50 publications

(36 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The perception of being good at an activity and the perception of rapid improvement both contribute positively to user engagement (Whitton 2011). Coyne (2003) analysed the design and characteristics of various existing games and found repetition as one of the main factors of engaging games, which is usually concealed through variation either in the form of difficulty levels (new opponents, track, etc.) and/or through a narrative.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Our user model aims to adapt the game challenge-path of the track-according to three concepts adopted from the concept of flow (Csikszentmihalyi 1990): Experience, Exploration and Attention.…”

Section: Background and Motivationmentioning

confidence: 99%

“…There is intense competition among game companies as they are being faced with challenges to retain user engagement in a saturated market. Steels (2004), based on the work done by Csikszentmihalyi (2000), suggests that for an activity to be self-motivating or "autotelic", there must be a balance between task challenge and the person's skill. Estimating the skills of the player and adapting the game challenge accordingly can lead to more engaging and immersing games.…”

Section: Introductionmentioning

confidence: 99%

“…Several machine learning techniques, such as Affinity Propagation (Frey and Dueck 2007) and Random Forests (Breiman 2001) are utilised to process the incoming data and transform them into available metrics, coupled with a weighting model (such as linear regression) that specifies their significance to the particular user. Finally, the higher layers are built up using ideas from educational theoretical frameworks such as the concept of flow (Csikszentmihalyi 1990), Zone Theory (Valsiner 1997) and the Zone of Proximal development (Vygotsky 1978); these layers provide path altering decisions for the particular user.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive user modelling in car racing games using behavioural and physiological data

Georgiou

Demiris

2017

User Model User-Adap Inter

View full text Add to dashboard Cite

Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to identify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user's physiological data and game-related actions, and cluster them into low level primitives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feedback from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions personalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation.

show abstract

Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive user modelling in car racing games using behavioural and physiological data

Georgiou

Demiris

2017

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…Related arguments suggest that digital media, and even computer games (this might come back to our argument about what a game is) can be used as a means of developing player confidence and abilities in spatial modeling, design composition, and form creation for application in architecture and the like. 47,48 Along the same lines, Guy et al…”

mentioning

confidence: 74%

Decision insight into stakeholder conflict for ERN.

Siirola¹,

Tidwell²,

Benz³

et al. 2012

View full text Add to dashboard Cite

Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effects in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming.This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: 1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, 42) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and 3) Examine potential applications of this technology and process.The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface.The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decisionmaking in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.

show abstract

The Digital Uncanny

Coyne

The Kluwer International Series on Computer Supported Cooperative Work

View full text Add to dashboard Cite

Mindless repetition: Learning from computer games

Cited by 50 publications

References 1 publication

Adaptive user modelling in car racing games using behavioural and physiological data

Adaptive user modelling in car racing games using behavioural and physiological data

Decision insight into stakeholder conflict for ERN.

The Digital Uncanny

Contact Info

Product

Resources

About