Erik Harpstead
Thomas Zimmermann
Nachiappan Nagapan
Ryan Cooper
ABSTRACT
A central interest of game designers and game user researchers is to understand why players enjoy their games. While a number of researchers have explored player enjoyment in general, few have talked about methods for enabling designers to understand the players of their specific game. In this paper we explore the creation of engagement profiles of game players based on log data. These profiles take into account the different ways that players engage with the game and highlight patterns associated with active play. We demonstrate our approach by performing a descriptive analysis of the game Forza Motorsport 5 using data from a sample of 1.2 million users of the game and discuss the implications of our findings.
- Alix, A.Beyond P-1: Who Plays Online? Proc. DiGRA 2005, (2005).Google Scholar
- Bartle, R.Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD Research 1, 1 (1996), 19.Google ScholarCross Ref
- Bauckhage, C., Kersting, K., Sifa, R., Thurau, C., Drachen, A., and Canossa, A.How Players Lose Interest in Playing a Game?: An Empirical Study Based on Distributions of Total Playing Times. Proc. CIG 2012, IEEE (2012), 139--146.Google ScholarCross Ref
- Bhatnagar, A. and Ghose, S.A latent class segmentation analysis of e-shoppers. Journal of Business Research 57, 7 (2004), 758--767.Google ScholarCross Ref
- Bouvier, P., Sehaba, K., and Lavoué, É.A trace-based approach to identifying users' engagement and qualifying their engaged-behaviours in interactive systems: application to a social game. User Modeling and User-Adapted Interaction 24, 5 (2014), 413--451. Google ScholarDigital Library
- Boyle, E. a., Connolly, T.M., Hainey, T., and Boyle, J.M.Engagement in digital entertainment games: A systematic review. Computers in Human Behavior 28, 3 (2012), 771--780. Google ScholarDigital Library
- Canossa, A. and Drachen, A.Patterns of Play: PlayPersonas in User-Centred Game Development. Proc. DiGRA 2009, (2009).Google Scholar
- Clauzel, D., Sehaba, K., and Prié, Y.Enhancing synchronous collaboration by using interactive visualisation of modelled traces. Simul.Model. Pract. Theory 19, 1 (2011), 84--97.Google ScholarCross Ref
- Cooper, A.The Inmates Are Running the Asylum: Why high-tech products drive us crazy and how to restore sanity. Sams, 1999. Google ScholarDigital Library
- Davis, F.D., Bagozzi, R.P., and Warshaw, P.R.User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science 35, 8 (1989), 982--1003. Google ScholarDigital Library
- Debeauvais, T., Zimmermann, T., Nagappan, N., et al.Off With Their Assists: An Empirical Study of Driving Skill in Forza Motorsport 4. Proc. FDG 2014, (2014).Google Scholar
- Drachen, A., Canossa, A., and Yannakakis, G.N.Player modeling using self-organization in Tomb Raider: Underworld. Proc. CIG 2009, IEEE (2009), 1--8. Google ScholarDigital Library
- Drachen, A. and Canossa, A.Towards gameplay analysis via gameplay metrics. Proc. MindTrek 2009, ACM Press (2009), 202. Google ScholarDigital Library
- Dunn, O.J.Multiple Comparisons Among Means. Journal of the American Statistical Association 56, 293 (2014), 52--64.Google Scholar
- Friess, E.Personas and Decision Making in the Design Process: An Ethnographic Case Study. Proc. CHI 2012, ACM Press (2012), 1209--1218. Google ScholarDigital Library
- Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K., and Bauckhage, C.Predicting Player Churn in the Wild. Proc. CIG 2014, IEEE (2014), 1--8.Google ScholarCross Ref
- Higgs, M., Girolami, M., Morrison, A., and Chalmers, M.Analysing User Behaviour Through Dynamic Population Models. Ext. Abstracts CHI 2013, ACM Press (2013), 271--276. Google ScholarDigital Library
- Holmgård, C., Liapis, A., Togelius, J., and Yannakakis, G.N.Generative Agents for Player Decision Modeling in Games. Proc. FDG 2014, (2014).Google Scholar
- Hunicke, R., Leblanc, M., and Zubek, R.MDA?: A Formal Approach to Game Design and Game Research. Proc. of the AAAI Workshop on Challenges in Game AI, (2004), 1--5.Google Scholar
- Kaptelinin, V. and Nardi, B.A.Acting with Technology: Activity Theory and Interaction Design. MIT Press, Cambridge, MA, 2006. Google ScholarDigital Library
- Kaufman, L. and Rousseeuw, P.J.Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, 2009.Google Scholar
- Kim, J.H., Gunn, D. V, Schuh, E., Phillips, B.C., Pagulayan, R.J., and Wixon, D.Tracking Real-Time User Experience (TRUE): A comprehensive instrumentation solution for complex systems. Proc. CHI 2008, ACM Press (2008), 443--451. Google ScholarDigital Library
- McCullagh, P. and Nelder, J.A.Generalized Linear Models. Chapman and Hall, London, 1989.Google ScholarCross Ref
- McGinn, J. (Jen) and Kotamraju, N.Data-driven persona development. Proc. CHI 2008, ACM Press (2008), 1521. Google ScholarDigital Library
- Mekler, E.D., Bopp, J.A., Tuch, A.N., and Opwis, K.A systematic review of quantitative studies on the enjoyment of digital entertainment games. Proc. CHI 2014, ACM Press (2014), 927--936. Google ScholarDigital Library
- Microsoft Research.DrivatarTM . http://research.microsoft.com/en-us/projects/drivatar/.Google Scholar
- Nacke, L. and Drachen, A.Towards a Framework of Player Experience Research. Proc. EPEX 2011, (2011).Google Scholar
- O'Brien, R.M.A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 41, 5 (2007), 673--690.Google Scholar
- Ryan, R.M. and Deci, E.Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 55, (2000), 68--78.Google ScholarCross Ref
- Seif El-Nasr, M., Drachen, A., and Canossa, A., eds.Game Analytics: Maximizing the Value of Player Data. Springer, 2013. Google ScholarDigital Library
- Theng, Y.-L., Wanzhen, L., Zhiqiang, L., Robert, C., and Pallavi, R.Investigation of the antecedents and consequences of gamer satisfaction. Computers in Entertainment 9, 3 (2011), 1--26. Google ScholarDigital Library
- Tuunanen, J. and Hamari, J.Meta-synthesis of player typologies. Proc. Noridc DiGRA 2012, (2012).Google Scholar
- Williams, D., Yee, N., and Caplan, S.E. Who plays, how much, and why? Debunking the stereotypical gamer profile. Journal of Computer-Mediated Communication 13, 4 (2008), 993--1018.Google ScholarCross Ref
- Xbox Wire Staff.Forza Motorsport 5 Free Road America Track Add-On Now Available. 2014.Google Scholar
Index Terms
- What Drives People: Creating Engagement Profiles of Players from Game Log Data
Recommendations
Playstyles in Tetris: Beyond Player Skill, Score, and Competition
Entertainment Computing – ICEC 2022AbstractIn this study, we looked at playstyles in Tetris in order to explore how players construct meaning in abstract video games. To do this, we looked at playstyles that go beyond metrics related to player skill, score and competition. Indeed, non-...
Got skillz?: player matching, mastery, and engagement in skill-based games
Gamification '13: Proceedings of the First International Conference on Gameful Design, Research, and ApplicationsIn this paper we explore mastery, a powerful intrinsic motivator, and its relationship to player engagement in a mobile skill-based game platform (Skillz.com). More specifically, we present a unique Elo-based rating system for quickly matching players ...
I-dentity: concealing movement representation associations in games
CHI EA '14: CHI '14 Extended Abstracts on Human Factors in Computing SystemsThis paper details the design of i-dentity, a collaborative movement-based game where the game design deliberately conceals the players' associations to a digital representation. While movement-based digital games typically make it clear whose movement ...
Comments